Instruction: What field is the article from?

Input:
Title: Does Explainable AI Have Moral Value?
Abstract: Explainable AI (XAI) aims to bridge the gap between complex algorithmic
systems and human stakeholders. Current discourse often examines XAI in
isolation as either a technological tool, user interface, or policy mechanism.
This paper proposes a unifying ethical framework grounded in moral duties and
the concept of reciprocity. We argue that XAI should be appreciated not merely
as a right, but as part of our moral duties that helps sustain a reciprocal
relationship between humans affected by AI systems. This is because, we argue,
explanations help sustain constitutive symmetry and agency in AI-led
decision-making processes. We then assess leading XAI communities and reveal
gaps between the ideal of reciprocity and practical feasibility. Machine
learning offers useful techniques but overlooks evaluation and adoption
challenges. Human-computer interaction provides preliminary insights but
oversimplifies organizational contexts. Policies espouse accountability but
lack technical nuance. Synthesizing these views exposes barriers to
implementable, ethical XAI. Still, positioning XAI as a moral duty transcends
rights-based discourse to capture a more robust and complete moral picture.
This paper provides an accessible, detailed analysis elucidating the moral
value of explainability.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Kantian Deontology Meets AI Alignment: Towards Morally Robust Fairness Metrics
Abstract: Deontological ethics, specifically understood through Immanuel Kant, provides
a moral framework that emphasizes the importance of duties and principles,
rather than the consequences of action. Understanding that despite the
prominence of deontology, it is currently an overlooked approach in fairness
metrics, this paper explores the compatibility of a Kantian deontological
framework in fairness metrics, part of the AI alignment field. We revisit
Kant's critique of utilitarianism, which is the primary approach in AI fairness
metrics and argue that fairness principles should align with the Kantian
deontological framework. By integrating Kantian ethics into AI alignment, we
not only bring in a widely-accepted prominent moral theory but also strive for
a more morally grounded AI landscape that better balances outcomes and
procedures in pursuit of fairness and justice.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: A Definition of Open-Ended Learning Problems for Goal-Conditioned Agents
Abstract: A lot of recent machine learning research papers have "Open-ended learning"
in their title. But very few of them attempt to define what they mean when
using the term. Even worse, when looking more closely there seems to be no
consensus on what distinguishes open-ended learning from related concepts such
as continual learning, lifelong learning or autotelic learning. In this paper,
we contribute to fixing this situation. After illustrating the genealogy of the
concept and more recent perspectives about what it truly means, we outline that
open-ended learning is generally conceived as a composite notion encompassing a
set of diverse properties. In contrast with these previous approaches, we
propose to isolate a key elementary property of open-ended processes, which is
to always produce novel elements from time to time over an infinite horizon.
From there, we build the notion of open-ended learning problems and focus in
particular on the subset of open-ended goal-conditioned reinforcement learning
problems, as this framework facilitates the definition of learning a growing
repertoire of skills. Finally, we highlight the work that remains to be
performed to fill the gap between our elementary definition and the more
involved notions of open-ended learning that developmental AI researchers may
have in mind.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Anti-LM Decoding for Zero-shot In-context Machine Translation
Abstract: Zero-shot In-context learning is the phenomenon where models can perform the
task simply given the instructions. However, pre-trained large language models
are known to be poorly calibrated for this task. One of the most effective
approaches to handling this bias is to adopt a contrastive decoding objective,
which accounts for the prior probability of generating the next token by
conditioning on some context. This work introduces an Anti-Language Model
objective with a decay factor designed to address the weaknesses of In-context
Machine Translation. We conduct our experiments across 3 model types and sizes,
3 language directions, and for both greedy decoding and beam search ($B=5$).
The proposed method outperforms other state-of-art decoding objectives, with up
to $20$ BLEU point improvement from the default objective observed in some
settings.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: AI capabilities can be significantly improved without expensive retraining
Abstract: State-of-the-art AI systems can be significantly improved without expensive
retraining via "post-training enhancements"-techniques applied after initial
training like fine-tuning the system to use a web browser. We review recent
post-training enhancements, categorizing them into five types: tool-use,
prompting methods, scaffolding, solution selection, and data generation.
Different enhancements improve performance on different tasks, making it hard
to compare their significance. So we translate improvements from different
enhancements into a common currency, the compute-equivalent gain: how much
additional training compute would be needed to improve performance by the same
amount as the enhancement. Our non-experimental work shows that post-training
enhancements have significant benefits: most surveyed enhancements improve
benchmark performance by more than a 5x increase in training compute, some by
more than 20x. Post-training enhancements are relatively cheap to develop:
fine-tuning costs are typically <1% of the original training cost. Governing
the development of capable post-training enhancements may be challenging
because frontier models could be enhanced by a wide range of actors.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Conditional Modeling Based Automatic Video Summarization
Abstract: The aim of video summarization is to shorten videos automatically while
retaining the key information necessary to convey the overall story. Video
summarization methods mainly rely on visual factors, such as visual
consecutiveness and diversity, which may not be sufficient to fully understand
the content of the video. There are other non-visual factors, such as
interestingness, representativeness, and storyline consistency that should also
be considered for generating high-quality video summaries. Current methods do
not adequately take into account these non-visual factors, resulting in
suboptimal performance. In this work, a new approach to video summarization is
proposed based on insights gained from how humans create ground truth video
summaries. The method utilizes a conditional modeling perspective and
introduces multiple meaningful random variables and joint distributions to
characterize the key components of video summarization. Helper distributions
are employed to improve the training of the model. A conditional attention
module is designed to mitigate potential performance degradation in the
presence of multi-modal input. The proposed video summarization method
incorporates the above innovative design choices that aim to narrow the gap
between human-generated and machine-generated video summaries. Extensive
experiments show that the proposed approach outperforms existing methods and
achieves state-of-the-art performance on commonly used video summarization
datasets.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Benefits and Harms of Large Language Models in Digital Mental Health
Abstract: The past decade has been transformative for mental health research and
practice. The ability to harness large repositories of data, whether from
electronic health records (EHR), mobile devices, or social media, has revealed
a potential for valuable insights into patient experiences, promising early,
proactive interventions, as well as personalized treatment plans. Recent
developments in generative artificial intelligence, particularly large language
models (LLMs), show promise in leading digital mental health to uncharted
territory. Patients are arriving at doctors' appointments with information
sourced from chatbots, state-of-the-art LLMs are being incorporated in medical
software and EHR systems, and chatbots from an ever-increasing number of
startups promise to serve as AI companions, friends, and partners. This article
presents contemporary perspectives on the opportunities and risks posed by LLMs
in the design, development, and implementation of digital mental health tools.
We adopt an ecological framework and draw on the affordances offered by LLMs to
discuss four application areas -- care-seeking behaviors from individuals in
need of care, community care provision, institutional and medical care
provision, and larger care ecologies at the societal level. We engage in a
thoughtful consideration of whether and how LLM-based technologies could or
should be employed for enhancing mental health. The benefits and harms our
article surfaces could serve to help shape future research, advocacy, and
regulatory efforts focused on creating more responsible, user-friendly,
equitable, and secure LLM-based tools for mental health treatment and
intervention.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: An Invitation to Deep Reinforcement Learning
Abstract: Training a deep neural network to maximize a target objective has become the
standard recipe for successful machine learning over the last decade. These
networks can be optimized with supervised learning, if the target objective is
differentiable. For many interesting problems, this is however not the case.
Common objectives like intersection over union (IoU), bilingual evaluation
understudy (BLEU) score or rewards cannot be optimized with supervised
learning. A common workaround is to define differentiable surrogate losses,
leading to suboptimal solutions with respect to the actual objective.
Reinforcement learning (RL) has emerged as a promising alternative for
optimizing deep neural networks to maximize non-differentiable objectives in
recent years. Examples include aligning large language models via human
feedback, code generation, object detection or control problems. This makes RL
techniques relevant to the larger machine learning audience. The subject is,
however, time intensive to approach due to the large range of methods, as well
as the often very theoretical presentation. In this introduction, we take an
alternative approach, different from classic reinforcement learning textbooks.
Rather than focusing on tabular problems, we introduce reinforcement learning
as a generalization of supervised learning, which we first apply to
non-differentiable objectives and later to temporal problems. Assuming only
basic knowledge of supervised learning, the reader will be able to understand
state-of-the-art deep RL algorithms like proximal policy optimization (PPO)
after reading this tutorial.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: When Large Language Models contradict humans? Large Language Models' Sycophantic Behaviour
Abstract: Large Language Models (LLMs) have been demonstrating the ability to solve
complex tasks by delivering answers that are positively evaluated by humans due
in part to the intensive use of human feedback that refines responses. However,
the suggestibility transmitted through human feedback increases the inclination
to produce responses that correspond to the user's beliefs or misleading
prompts as opposed to true facts, a behaviour known as sycophancy. This
phenomenon decreases the bias, robustness, and, consequently, their
reliability.
  In this paper, we shed light on the suggestibility of LLMs to sycophantic
behaviour, demonstrating these tendencies via human-influenced prompts over
different tasks. Our investigation reveals that LLMs show sycophantic
tendencies when responding to queries involving subjective opinions and
statements that should elicit a contrary response based on facts, demonstrating
a lack of robustness.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: The Evolution of the Interplay Between Input Distributions and Linear Regions in Networks
Abstract: It is commonly recognized that the expressiveness of deep neural networks is
contingent upon a range of factors, encompassing their depth, width, and other
relevant considerations. Currently, the practical performance of the majority
of deep neural networks remains uncertain. For ReLU (Rectified Linear Unit)
networks with piecewise linear activations, the number of linear convex regions
serves as a natural metric to gauge the network's expressivity. In this paper,
we count the number of linear convex regions in deep neural networks based on
ReLU. In particular, we prove that for any one-dimensional input, there exists
a minimum threshold for the number of neurons required to express it. We also
empirically observe that for the same network, intricate inputs hinder its
capacity to express linear regions. Furthermore, we unveil the iterative
refinement process of decision boundaries in ReLU networks during training. We
aspire for our research to serve as an inspiration for network optimization
endeavors and aids in the exploration and analysis of the behaviors exhibited
by deep networks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting
Abstract: A crucial challenge for generative large language models (LLMs) is diversity:
when a user's prompt is under-specified, models may follow implicit assumptions
while generating a response, which may result in homogenization of the
responses, as well as certain demographic groups being under-represented or
even erased from the generated responses. In this paper, we formalize diversity
of representation in generative LLMs. We present evaluation datasets and
propose metrics to measure diversity in generated responses along people and
culture axes. We find that LLMs understand the notion of diversity, and that
they can reason and critique their own responses for that goal. This finding
motivated a new prompting technique called collective-critique and self-voting
(CCSV) to self-improve people diversity of LLMs by tapping into its diversity
reasoning capabilities, without relying on handcrafted examples or prompt
tuning. Extensive empirical experiments with both human and automated
evaluations show that our proposed approach is effective at improving people
and culture diversity, and outperforms all baseline methods by a large margin.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Active Reasoning in an Open-World Environment
Abstract: Recent advances in vision-language learning have achieved notable success on
complete-information question-answering datasets through the integration of
extensive world knowledge. Yet, most models operate passively, responding to
questions based on pre-stored knowledge. In stark contrast, humans possess the
ability to actively explore, accumulate, and reason using both newfound and
existing information to tackle incomplete-information questions. In response to
this gap, we introduce $Conan$, an interactive open-world environment devised
for the assessment of active reasoning. $Conan$ facilitates active exploration
and promotes multi-round abductive inference, reminiscent of rich, open-world
settings like Minecraft. Diverging from previous works that lean primarily on
single-round deduction via instruction following, $Conan$ compels agents to
actively interact with their surroundings, amalgamating new evidence with prior
knowledge to elucidate events from incomplete observations. Our analysis on
$Conan$ underscores the shortcomings of contemporary state-of-the-art models in
active exploration and understanding complex scenarios. Additionally, we
explore Abduction from Deduction, where agents harness Bayesian rules to recast
the challenge of abduction as a deductive process. Through $Conan$, we aim to
galvanize advancements in active reasoning and set the stage for the next
generation of artificial intelligence agents adept at dynamically engaging in
environments.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Effective Quantization for Diffusion Models on CPUs
Abstract: Diffusion models have gained popularity for generating images from textual
descriptions. Nonetheless, the substantial need for computational resources
continues to present a noteworthy challenge, contributing to time-consuming
processes. Quantization, a technique employed to compress deep learning models
for enhanced efficiency, presents challenges when applied to diffusion models.
These models are notably more sensitive to quantization compared to other model
types, potentially resulting in a degradation of image quality. In this paper,
we introduce a novel approach to quantize the diffusion models by leveraging
both quantization-aware training and distillation. Our results show the
quantized models can maintain the high image quality while demonstrating the
inference efficiency on CPUs. The code is publicly available at:
https://github.com/intel/intel-extension-for-transformers.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
Abstract: Query expansion is a commonly-used technique in many search systems to better
represent users' information needs with additional query terms. Existing
studies for this task usually propose to expand a query with retrieved or
generated contextual documents. However, both types of methods have clear
limitations. For retrieval-based methods, the documents retrieved with the
original query might not be accurate enough to reveal the search intent,
especially when the query is brief or ambiguous. For generation-based methods,
existing models can hardly be trained or aligned on a particular corpus, due to
the lack of corpus-specific labeled data. In this paper, we propose a novel
Large Language Model (LLM) based mutual verification framework for query
expansion, which alleviates the aforementioned limitations. Specifically, we
first design a query-query-document generation pipeline, which can effectively
leverage the contextual knowledge encoded in LLMs to generate sub-queries and
corresponding documents from multiple perspectives. Next, we employ a mutual
verification method for both generated and retrieved contextual documents,
where 1) retrieved documents are filtered with the external contextual
knowledge in generated documents, and 2) generated documents are filtered with
the corpus-specific knowledge in retrieved documents. Overall, the proposed
method allows retrieved and generated documents to complement each other to
finalize a better query expansion. We conduct extensive experiments on three
information retrieval datasets, i.e., TREC-DL-2020, TREC-COVID, and MSMARCO.
The results demonstrate that our method outperforms other baselines
significantly.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
Abstract: Offline-to-online Reinforcement Learning (O2O RL) aims to improve the
performance of offline pretrained policy using only a few online samples. Built
on offline RL algorithms, most O2O methods focus on the balance between RL
objective and pessimism, or the utilization of offline and online samples. In
this paper, from a novel perspective, we systematically study the challenges
that remain in O2O RL and identify that the reason behind the slow improvement
of the performance and the instability of online finetuning lies in the
inaccurate Q-value estimation inherited from offline pretraining. Specifically,
we demonstrate that the estimation bias and the inaccurate rank of Q-value
cause a misleading signal for the policy update, making the standard offline RL
algorithms, such as CQL and TD3-BC, ineffective in the online finetuning. Based
on this observation, we address the problem of Q-value estimation by two
techniques: (1) perturbed value update and (2) increased frequency of Q-value
updates. The first technique smooths out biased Q-value estimation with sharp
peaks, preventing early-stage policy exploitation of sub-optimal actions. The
second one alleviates the estimation bias inherited from offline pretraining by
accelerating learning. Extensive experiments on the MuJoco and Adroit
environments demonstrate that the proposed method, named SO2, significantly
alleviates Q-value estimation issues, and consistently improves the performance
against the state-of-the-art methods by up to 83.1%.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: LLMs cannot find reasoning errors, but can correct them!
Abstract: While self-correction has shown promise in improving LLM outputs in terms of
style and quality (e.g. Chen et al., 2023; Madaan et al., 2023), recent
attempts to self-correct logical or reasoning errors often cause correct
answers to become incorrect, resulting in worse performances overall (Huang et
al., 2023). In this paper, we break down the self-correction process into two
core components: mistake finding and output correction. For mistake finding, we
release BIG-Bench Mistake, a dataset of logical mistakes in Chain-of-Thought
reasoning traces. We provide benchmark numbers for several state-of-the-art
LLMs, and demonstrate that LLMs generally struggle with finding logical
mistakes. For output correction, we propose a backtracking method which
provides large improvements when given information on mistake location. We
construe backtracking as a lightweight alternative to reinforcement learning
methods, and show that it remains effective with a reward model at 60-70%
accuracy.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Analyzing Emissions and Energy Efficiency in Mixed Traffic Control at Unsignalized Intersections
Abstract: Greenhouse gas emissions have dramatically risen since the early 1900s with
U.S. transportation generating 28% of the U.S' emissions. As such, there is
interest in reducing transportation-related emissions. Specifically,
sustainability research has sprouted around signalized intersections as
intersections allow different streams of traffic to cross and change
directions. Recent research has developed mixed traffic control eco-driving
strategies at signalized intersections to decrease emissions. However, the
inherent structure of a signalized intersection generates increased emissions
by creating frequent acceleration/deceleration events, excessive idling from
traffic congestion, and stop-and-go waves. Thus, we believe unsignalized
intersections hold potential for further sustainability improvements. In this
work, we provide an emissions analysis on unsignalized intersections with
complex, real-world topologies and traffic demands where mixed traffic control
strategies are employed by robot vehicles (RVs) to reduce waiting times and
congestion. We find with at least 10% RV penetration rate, RVs generate less
fuel consumption and NOx emissions than signalized intersections by up to 27%
and 28%, respectively. With at least 30% RVs, CO and HC emissions are reduced
by up to 42% and 43%, respectively. Additionally, RVs can reduce emissions
across the whole network despite only employing their strategies at the
intersections.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: PartialFormer: Modeling Part Instead of Whole
Abstract: The design choices in Transformer feed-forward neural networks have resulted
in significant computational and parameter overhead. In this work, we emphasize
the importance of hidden dimension in designing lightweight FFNs, a factor
often overlooked in previous architectures. Guided by this principle, we
introduce PartialFormer, a parameter-efficient Transformer architecture
utilizing multiple smaller FFNs to reduce parameters and computation while
maintaining essential hidden dimensions. These smaller FFNs are integrated into
a multi-head attention system to enable effective collaboration. We also
propose a tailored head scaling strategy to enhance PartialFormer's
capabilities. Furthermore, we present a residual-like attention calculation to
improve depth scaling within PartialFormer. Extensive experiments on 9
translation tasks and 1 abstractive summarization task validate the
effectiveness of our PartialFormer approach. Our code would be available at:
\url{https://github.com/zhengkid/PartialFormer}.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences
Abstract: Customizing robotic behaviors to be aligned with diverse human preferences is
an underexplored challenge in the field of embodied AI. In this paper, we
present Promptable Behaviors, a novel framework that facilitates efficient
personalization of robotic agents to diverse human preferences in complex
environments. We use multi-objective reinforcement learning to train a single
policy adaptable to a broad spectrum of preferences. We introduce three
distinct methods to infer human preferences by leveraging different types of
interactions: (1) human demonstrations, (2) preference feedback on trajectory
comparisons, and (3) language instructions. We evaluate the proposed method in
personalized object-goal navigation and flee navigation tasks in ProcTHOR and
RoboTHOR, demonstrating the ability to prompt agent behaviors to satisfy human
preferences in various scenarios. Project page:
https://promptable-behaviors.github.io

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Artificial Intelligence and Human Geography
Abstract: This paper examines the recent advances and applications of AI in human
geography especially the use of machine (deep) learning, including place
representation and modeling, spatial analysis and predictive mapping, and urban
planning and design. AI technologies have enabled deeper insights into complex
human-environment interactions, contributing to more effective scientific
exploration, understanding of social dynamics, and spatial decision-making.
Furthermore, human geography offers crucial contributions to AI, particularly
in context-aware model development, human-centered design, biases and ethical
considerations, and data privacy. The synergy beween AI and human geography is
essential for addressing global challenges like disaster resilience, poverty,
and equitable resource access. This interdisciplinary collaboration between AI
and geography will help advance the development of GeoAI and promise a better
and sustainable world for all.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Learning to Fly in Seconds
Abstract: Learning-based methods, particularly Reinforcement Learning (RL), hold great
promise for streamlining deployment, enhancing performance, and achieving
generalization in the control of autonomous multirotor aerial vehicles. Deep RL
has been able to control complex systems with impressive fidelity and agility
in simulation but the simulation-to-reality transfer often brings a
hard-to-bridge reality gap. Moreover, RL is commonly plagued by prohibitively
long training times. In this work, we propose a novel asymmetric
actor-critic-based architecture coupled with a highly reliable RL-based
training paradigm for end-to-end quadrotor control. We show how curriculum
learning and a highly optimized simulator enhance sample complexity and lead to
fast training times. To precisely discuss the challenges related to
low-level/end-to-end multirotor control, we also introduce a taxonomy that
classifies the existing levels of control abstractions as well as
non-linearities and domain parameters. Our framework enables
Simulation-to-Reality (Sim2Real) transfer for direct RPM control after only 18
seconds of training on a consumer-grade laptop as well as its deployment on
microcontrollers to control a multirotor under real-time guarantees. Finally,
our solution exhibits competitive performance in trajectory tracking, as
demonstrated through various experimental comparisons with existing
state-of-the-art control solutions using a real Crazyflie nano quadrotor. We
open source the code including a very fast multirotor dynamics simulator that
can simulate about 5 months of flight per second on a laptop GPU. The fast
training times and deployment to a cheap, off-the-shelf quadrotor lower the
barriers to entry and help democratize the research and development of these
systems.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction
Abstract: Trajectory prediction in traffic scenes involves accurately forecasting the
behaviour of surrounding vehicles. To achieve this objective it is crucial to
consider contextual information, including the driving path of vehicles, road
topology, lane dividers, and traffic rules. Although studies demonstrated the
potential of leveraging heterogeneous context for improving trajectory
prediction, state-of-the-art deep learning approaches still rely on a limited
subset of this information. This is mainly due to the limited availability of
comprehensive representations. This paper presents an approach that utilizes
knowledge graphs to model the diverse entities and their semantic connections
within traffic scenes. Further, we present nuScenes Knowledge Graph (nSKG), a
knowledge graph for the nuScenes dataset, that models explicitly all scene
participants and road elements, as well as their semantic and spatial
relationships. To facilitate the usage of the nSKG via graph neural networks
for trajectory prediction, we provide the data in a format, ready-to-use by the
PyG library. All artefacts can be found here:
https://github.com/boschresearch/nuScenes_Knowledge_Graph

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: CodeFusion: A Pre-trained Diffusion Model for Code Generation
Abstract: Imagine a developer who can only change their last line of code, how often
would they have to start writing a function from scratch before it is correct?
Auto-regressive models for code generation from natural language have a similar
limitation: they do not easily allow reconsidering earlier tokens generated. We
introduce CodeFusion, a pre-trained diffusion code generation model that
addresses this limitation by iteratively denoising a complete program
conditioned on the encoded natural language. We evaluate CodeFusion on the task
of natural language to code generation for Bash, Python, and Microsoft Excel
conditional formatting (CF) rules. Experiments show that CodeFusion (75M
parameters) performs on par with state-of-the-art auto-regressive systems
(350M-175B parameters) in top-1 accuracy and outperforms them in top-3 and
top-5 accuracy due to its better balance in diversity versus quality.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: FAME: Flexible, Scalable Analogy Mappings Engine
Abstract: Analogy is one of the core capacities of human cognition; when faced with new
situations, we often transfer prior experience from other domains. Most work on
computational analogy relies heavily on complex, manually crafted input. In
this work, we relax the input requirements, requiring only names of entities to
be mapped. We automatically extract commonsense representations and use them to
identify a mapping between the entities. Unlike previous works, our framework
can handle partial analogies and suggest new entities to be added. Moreover,
our method's output is easily interpretable, allowing for users to understand
why a specific mapping was chosen.
  Experiments show that our model correctly maps 81.2% of classical 2x2 analogy
problems (guess level=50%). On larger problems, it achieves 77.8% accuracy
(mean guess level=13.1%). In another experiment, we show our algorithm
outperforms human performance, and the automatic suggestions of new entities
resemble those suggested by humans. We hope this work will advance
computational analogy by paving the way to more flexible, realistic input
requirements, with broader applicability.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders
Abstract: Prevailing research practice today often relies on training dense retrievers
on existing large datasets such as MSMARCO and then experimenting with ways to
improve zero-shot generalization capabilities to unseen domains. While prior
work has tackled this challenge through resource-intensive steps such as data
augmentation, architectural modifications, increasing model size, or even
further base model pretraining, comparatively little investigation has examined
whether the training procedures themselves can be improved to yield better
generalization capabilities in the resulting models. In this work, we recommend
a simple recipe for training dense encoders: Train on MSMARCO with
parameter-efficient methods, such as LoRA, and opt for using in-batch negatives
unless given well-constructed hard negatives. We validate these recommendations
using the BEIR benchmark and find results are persistent across choice of dense
encoder and base model size and are complementary to other resource-intensive
strategies for out-of-domain generalization such as architectural modifications
or additional pretraining. We hope that this thorough and impartial study
around various training techniques, which augments other resource-intensive
methods, offers practical insights for developing a dense retrieval model that
effectively generalizes, even when trained on a single dataset.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Distantly-Supervised Named Entity Recognition with Uncertainty-aware Teacher Learning and Student-student Collaborative Learning
Abstract: Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates
the burden of annotation, but meanwhile suffers from the label noise. Recent
works attempt to adopt the teacher-student framework to gradually refine the
training labels and improve the overall robustness. However, we argue that
these teacher-student methods achieve limited performance because poor network
calibration produces incorrectly pseudo-labeled samples, leading to error
propagation. Therefore, we attempt to mitigate this issue by proposing: (1)
Uncertainty-aware Teacher Learning that leverages the prediction uncertainty to
guide the selection of pseudo-labels, avoiding the number of incorrect
pseudo-labels in the self-training stage. (2) Student-student Collaborative
Learning that allows the transfer of reliable labels between two student
networks instead of completely relying on all pseudo-labels from its teacher.
Meanwhile, this approach allows a full exploration of mislabeled samples rather
than simply filtering unreliable pseudo-labeled samples. Extensive experimental
results on five DS-NER datasets demonstrate that our method is superior to
state-of-the-art teacher-student methods.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
Abstract: ReParameterization (RP) Policy Gradient Methods (PGMs) have been widely
adopted for continuous control tasks in robotics and computer graphics.
However, recent studies have revealed that, when applied to long-term
reinforcement learning problems, model-based RP PGMs may experience chaotic and
non-smooth optimization landscapes with exploding gradient variance, which
leads to slow convergence. This is in contrast to the conventional belief that
reparameterization methods have low gradient estimation variance in problems
such as training deep generative models. To comprehend this phenomenon, we
conduct a theoretical examination of model-based RP PGMs and search for
solutions to the optimization difficulties. Specifically, we analyze the
convergence of the model-based RP PGMs and pinpoint the smoothness of function
approximators as a major factor that affects the quality of gradient
estimation. Based on our analysis, we propose a spectral normalization method
to mitigate the exploding variance issue caused by long model unrolls. Our
experimental results demonstrate that proper normalization significantly
reduces the gradient variance of model-based RP PGMs. As a result, the
performance of the proposed method is comparable or superior to other gradient
estimators, such as the Likelihood Ratio (LR) gradient estimator. Our code is
available at https://github.com/agentification/RP_PGM.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Conditional Prompt Tuning for Multimodal Fusion
Abstract: We show that the representation of one modality can effectively guide the
prompting of another modality for parameter-efficient multimodal fusion.
Specifically, we first encode one modality and use its representation as a
prior to conditionally prompt all frozen layers of the other modality. This is
achieved by disentangling the vanilla prompt vectors into three types of
specialized prompts that adaptively capture global-level and instance-level
features. To better produce the instance-wise prompt, we introduce the mixture
of prompt experts (MoPE) to dynamically route each instance to the most
suitable prompt experts for encoding. We further study a regularization term to
avoid degenerated prompt expert routing. Thanks to our design, our method can
effectively transfer the pretrained knowledge in unimodal encoders for
downstream multimodal tasks. Compared with vanilla prompting, we show that our
MoPE-based conditional prompting is more expressive, thereby scales better with
training data and the total number of prompts. We also demonstrate that our
prompt tuning is architecture-agnostic, thereby offering high modularity.
Extensive experiments over three multimodal datasets demonstrate
state-of-the-art results, matching or surpassing the performance achieved
through fine-tuning, while only necessitating 0.7% of the trainable parameters.
Code will be released: https://github.com/songrise/ConditionalPrompt.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
Abstract: This paper proposes a novel contrastive learning framework, called FOCAL, for
extracting comprehensive features from multimodal time-series sensing signals
through self-supervised training. Existing multimodal contrastive frameworks
mostly rely on the shared information between sensory modalities, but do not
explicitly consider the exclusive modality information that could be critical
to understanding the underlying sensing physics. Besides, contrastive
frameworks for time series have not handled the temporal information locality
appropriately. FOCAL solves these challenges by making the following
contributions: First, given multimodal time series, it encodes each modality
into a factorized latent space consisting of shared features and private
features that are orthogonal to each other. The shared space emphasizes feature
patterns consistent across sensory modalities through a modal-matching
objective. In contrast, the private space extracts modality-exclusive
information through a transformation-invariant objective. Second, we propose a
temporal structural constraint for modality features, such that the average
distance between temporally neighboring samples is no larger than that of
temporally distant samples. Extensive evaluations are performed on four
multimodal sensing datasets with two backbone encoders and two classifiers to
demonstrate the superiority of FOCAL. It consistently outperforms the
state-of-the-art baselines in downstream tasks with a clear margin, under
different ratios of available labels. The code and self-collected dataset are
available at https://github.com/tomoyoshki/focal.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
Abstract: In this paper, we present SwiftLearn, a data-efficient approach to accelerate
training of deep learning models using a subset of data samples selected during
the warm-up stages of training. This subset is selected based on an importance
criteria measured over the entire dataset during warm-up stages, aiming to
preserve the model performance with fewer examples during the rest of training.
The importance measure we propose could be updated during training every once
in a while, to make sure that all of the data samples have a chance to return
to the training loop if they show a higher importance. The model architecture
is unchanged but since the number of data samples controls the number of
forward and backward passes during training, we can reduce the training time by
reducing the number of training samples used in each epoch of training.
Experimental results on a variety of CV and NLP models during both pretraining
and finetuning show that the model performance could be preserved while
achieving a significant speed-up during training. More specifically, BERT
finetuning on GLUE benchmark shows that almost 90% of the data can be dropped
achieving an end-to-end average speedup of 3.36x while keeping the average
accuracy drop less than 0.92%.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: COVID-19 Imposes Rethinking of Conferencing -- Environmental Impact Assessment of Artificial Intelligence Conferences
Abstract: It has been noticed that through COVID-19 greenhouse gas emissions had a
sudden reduction. Based on this significant observation, we decided to conduct
a research to quantify the impact of scientific conferences' air-travelling,
explore and suggest alternative ways for greener conferences to re-duce the
global carbon footprint. Specifically, we focused on the most popular
conferences for the Artificial Intelligence community based on their scientific
impact factor, their scale, and the well-organized proceedings towards
measuring the impact of air travelling participation. This is the first time
that systematic quantification of a state-of-the-art subject like Artificial
Intelligence takes place to define its conferencing footprint in the broader
frames of environmental awareness. Our findings highlight that the virtual way
is the first on the list of green conferences' conduction although there are
serious concerns about it. Alternatives to optimal conferences' location
selection have demonstrated savings on air-travelling CO2 emissions of up to
63.9%.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators
Abstract: We adapt Parameterized Environment Response Model (PERM), a method for
training both Reinforcement Learning (RL) Agents and human learners in
parameterized environments by directly modeling difficulty and ability.
Inspired by Item Response Theory (IRT), PERM aligns environment difficulty with
individual ability, creating a Zone of Proximal Development-based curriculum.
Remarkably, PERM operates without real-time RL updates and allows for offline
training, ensuring its adaptability across diverse students. We present a
two-stage training process that capitalizes on PERM's adaptability, and
demonstrate its effectiveness in training RL agents and humans in an empirical
study.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Managing AI Risks in an Era of Rapid Progress
Abstract: In this short consensus paper, we outline risks from upcoming, advanced AI
systems. We examine large-scale social harms and malicious uses, as well as an
irreversible loss of human control over autonomous AI systems. In light of
rapid and continuing AI progress, we propose urgent priorities for AI R&D and
governance.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Anytime Approximate Formal Feature Attribution
Abstract: Widespread use of artificial intelligence (AI) algorithms and machine
learning (ML) models on the one hand and a number of crucial issues pertaining
to them warrant the need for explainable artificial intelligence (XAI). A key
explainability question is: given this decision was made, what are the input
features which contributed to the decision? Although a range of XAI approaches
exist to tackle this problem, most of them have significant limitations.
Heuristic XAI approaches suffer from the lack of quality guarantees, and often
try to approximate Shapley values, which is not the same as explaining which
features contribute to a decision. A recent alternative is so-called formal
feature attribution (FFA), which defines feature importance as the fraction of
formal abductive explanations (AXp's) containing the given feature. This
measures feature importance from the view of formally reasoning about the
model's behavior. It is challenging to compute FFA using its definition because
that involves counting AXp's, although one can approximate it. Based on these
results, this paper makes several contributions. First, it gives compelling
evidence that computing FFA is intractable, even if the set of contrastive
formal explanations (CXp's) is provided, by proving that the problem is
#P-hard. Second, by using the duality between AXp's and CXp's, it proposes an
efficient heuristic to switch from CXp enumeration to AXp enumeration
on-the-fly resulting in an adaptive explanation enumeration algorithm
effectively approximating FFA in an anytime fashion. Finally, experimental
results obtained on a range of widely used datasets demonstrate the
effectiveness of the proposed FFA approximation approach in terms of the error
of FFA approximation as well as the number of explanations computed and their
diversity given a fixed time limit.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes
Abstract: Federated learning shows promise as a privacy-preserving collaborative
learning technique. Existing heterogeneous federated learning mainly focuses on
skewing the label distribution across clients. However, most approaches suffer
from catastrophic forgetting and concept drift, mainly when the global
distribution of all classes is extremely unbalanced and the data distribution
of the client dynamically evolves over time. In this paper, we study the new
task, i.e., Dynamic Heterogeneous Federated Learning (DHFL), which addresses
the practical scenario where heterogeneous data distributions exist among
different clients and dynamic tasks within the client. Accordingly, we propose
a novel federated learning framework named Federated Multi-Level Prototypes
(FedMLP) and design federated multi-level regularizations. To mitigate concept
drift, we construct prototypes and semantic prototypes to provide fruitful
generalization knowledge and ensure the continuity of prototype spaces. To
maintain the model stability and consistency of convergence, three
regularizations are introduced as training losses, i.e., prototype-based
regularization, semantic prototype-based regularization, and federated
inter-task regularization. Extensive experiments show that the proposed method
achieves state-of-the-art performance in various settings.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning
Abstract: We present Emu Video, a text-to-video generation model that factorizes the
generation into two steps: first generating an image conditioned on the text,
and then generating a video conditioned on the text and the generated image. We
identify critical design decisions--adjusted noise schedules for diffusion, and
multi-stage training--that enable us to directly generate high quality and high
resolution videos, without requiring a deep cascade of models as in prior work.
In human evaluations, our generated videos are strongly preferred in quality
compared to all prior work--81% vs. Google's Imagen Video, 90% vs. Nvidia's
PYOCO, and 96% vs. Meta's Make-A-Video. Our model outperforms commercial
solutions such as RunwayML's Gen2 and Pika Labs. Finally, our factorizing
approach naturally lends itself to animating images based on a user's text
prompt, where our generations are preferred 96% over prior work.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: SegMix: A Simple Structure-Aware Data Augmentation Method
Abstract: Interpolation-based Data Augmentation (DA) methods (Mixup) linearly
interpolate the inputs and labels of two or more training examples. Mixup has
more recently been adapted to the field of Natural Language Processing (NLP),
mainly for sequence labeling tasks. However, such a simple adoption yields
mixed or unstable improvements over the baseline models. We argue that the
direct-adoption methods do not account for structures in NLP tasks. To this
end, we propose SegMix, a collection of interpolation-based DA algorithms that
can adapt to task-specific structures. SegMix poses fewer constraints on data
structures, is robust to various hyperparameter settings, applies to more task
settings, and adds little computational overhead. In the algorithm's core, we
apply interpolation methods on task-specific meaningful segments, in contrast
to applying them on sequences as in prior work. We find SegMix to be a flexible
framework that combines rule-based DA methods with interpolation-based methods,
creating interesting mixtures of DA techniques. We show that SegMix
consistently improves performance over strong baseline models in Named Entity
Recognition (NER) and Relation Extraction (RE) tasks, especially under
data-scarce settings. Furthermore, this method is easy to implement and adds
negligible training overhead.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Testing LLMs on Code Generation with Varying Levels of Prompt Specificity
Abstract: Large language models (LLMs) have demonstrated unparalleled prowess in
mimicking human-like text generation and processing. Among the myriad of
applications that benefit from LLMs, automated code generation is increasingly
promising. The potential to transform natural language prompts into executable
code promises a major shift in software development practices and paves the way
for significant reductions in manual coding efforts and the likelihood of
human-induced errors. This paper reports the results of a study that evaluates
the performance of various LLMs, such as Bard, ChatGPT-3.5, ChatGPT-4, and
Claude-2, in generating Python for coding problems. We focus on how levels of
prompt specificity impact the accuracy, time efficiency, and space efficiency
of the generated code. A benchmark of 104 coding problems, each with four types
of prompts with varying degrees of tests and specificity, was employed to
examine these aspects comprehensively. Our results indicate significant
variations in performance across different LLMs and prompt types, and its key
contribution is to reveal the ideal prompting strategy for creating accurate
Python functions. This study lays the groundwork for further research in LLM
capabilities and suggests practical implications for utilizing LLMs in
automated code generation tasks and test-driven development.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Revisiting the Knowledge Injection Frameworks
Abstract: In recent years, large language models (LLMs), such as GPTs, have attained
great impact worldwide. However, how to adapt these LLMs to better suit the
vertical domain-specific tasks by utilizing external knowledge remains not
completely solved. Indeed, there have emerged a few works on this line where
most of them rely on an alignment heuristic that is built to inject the
corresponding knowledge tuple into the associated text sample.
  However, despite the promise, we identify a pivotal problem in this work
ubiquitously. Simply put, we find that injecting unaligned (i.e., random)
knowledge tuple into the LLMs achieves comparable (and sometimes better)
results than the aligned knowledge being injected. We therefore take a thorough
investigation of this frustrating finding on a variety of related prior work
and further provide a chain of potential interpretations for the phenomenon.
Based on all that, we offer a simple remediated technique. Briefly, the core of
this technique is rooted in an ideological emphasis on the pruning and
purification of the external knowledge base to be injected into LLMs. At last,
we show that by integrating this technique into most (if not all) knowledge
injection frameworks and recent LLMs, it manages to overcome the aforementioned
sanity problem and further pushes the boundary of the performance of the
domain-adaptive LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: BARET : Balanced Attention based Real image Editing driven by Target-text Inversion
Abstract: Image editing approaches with diffusion models have been rapidly developed,
yet their applicability are subject to requirements such as specific editing
types (e.g., foreground or background object editing, style transfer), multiple
conditions (e.g., mask, sketch, caption), and time consuming fine-tuning of
diffusion models. For alleviating these limitations and realizing efficient
real image editing, we propose a novel editing technique that only requires an
input image and target text for various editing types including non-rigid edits
without fine-tuning diffusion model. Our method contains three novelties:(I)
Target-text Inversion Schedule (TTIS) is designed to fine-tune the input target
text embedding to achieve fast image reconstruction without image caption and
acceleration of convergence.(II) Progressive Transition Scheme applies
progressive linear interpolation between target text embedding and its
fine-tuned version to generate transition embedding for maintaining non-rigid
editing capability.(III) Balanced Attention Module (BAM) balances the tradeoff
between textual description and image semantics.By the means of combining
self-attention map from reconstruction process and cross-attention map from
transition process, the guidance of target text embeddings in diffusion process
is optimized.In order to demonstrate editing capability, effectiveness and
efficiency of the proposed BARET, we have conducted extensive qualitative and
quantitative experiments. Moreover, results derived from user study and
ablation study further prove the superiority over other methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A Quantitative Autonomy Quantification Framework for Fully Autonomous Robotic Systems
Abstract: Although autonomous functioning facilitates deployment of robotic systems in
domains that admit limited human oversight on our planet and beyond, finding
correspondence between task requirements and autonomous capability is still an
open challenge. Consequently, a number of methods for quantifying autonomy have
been proposed over the last three decades, but to our knowledge all these have
no discernment of sub-mode features of variation of autonomy and some are based
on metrics that violet the Goodhart's law. This paper focuses on the full
autonomous mode and proposes a task-requirements based autonomy assessment
framework. The framework starts by establishing robot task characteristics from
which three autonomy metrics, namely requisite capability, reliability and
responsiveness, and functions for determining autonomy as a two-part measure,
namely of level of autonomy and degree of autonomy are derived. These
characteristics are founded on the realization that robots ultimately replace
human skilled workers, to find a mapping between human job and robot task
characteristics. The distinction between level and degree of autonomy stemmed
from the acknowledgment that autonomy is not just a question of existence, but
also one of performance of requisite capability. When continuously monitored,
the proposed metrics provide a means of monitoring the integrity of a system.
The framework has been demonstrated on two case studies, namely autonomous
vehicle at an on-road dynamic driving task and the DARPA subT challenge rules
analysis. The framework provides not only a tool for quantifying autonomy, but
also a regulatory interface and common language for autonomous systems
developers and users.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Who is leading in AI? An analysis of industry AI research
Abstract: AI research is increasingly industry-driven, making it crucial to understand
company contributions to this field. We compare leading AI companies by
research publications, citations, size of training runs, and contributions to
algorithmic innovations. Our analysis reveals the substantial role played by
Google, OpenAI and Meta. We find that these three companies have been
responsible for some of the largest training runs, developed a large fraction
of the algorithmic innovations that underpin large language models, and led in
various metrics of citation impact. In contrast, leading Chinese companies such
as Tencent and Baidu had a lower impact on many of these metrics compared to US
counterparts. We observe many industry labs are pursuing large training runs,
and that training runs from relative newcomers -- such as OpenAI and Anthropic
-- have matched or surpassed those of long-standing incumbents such as Google.
The data reveals a diverse ecosystem of companies steering AI progress, though
US labs such as Google, OpenAI and Meta lead across critical metrics.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: LLM4Drive: A Survey of Large Language Models for Autonomous Driving
Abstract: Autonomous driving technology, a catalyst for revolutionizing transportation
and urban mobility, has the tend to transition from rule-based systems to
data-driven strategies. Traditional module-based systems are constrained by
cumulative errors among cascaded modules and inflexible pre-set rules. In
contrast, end-to-end autonomous driving systems have the potential to avoid
error accumulation due to their fully data-driven training process, although
they often lack transparency due to their "black box" nature, complicating the
validation and traceability of decisions. Recently, large language models
(LLMs) have demonstrated abilities including understanding context, logical
reasoning, and generating answers. A natural thought is to utilize these
abilities to empower autonomous driving. By combining LLM with foundation
vision models, it could open the door to open-world understanding, reasoning,
and few-shot learning, which current autonomous driving systems are lacking. In
this paper, we systematically review a research line about \textit{Large
Language Models for Autonomous Driving (LLM4AD)}. This study evaluates the
current state of technological advancements, distinctly outlining the principal
challenges and prospective directions for the field. For the convenience of
researchers in academia and industry, we provide real-time updates on the
latest advances in the field as well as relevant open-source resources via the
designated link: https://github.com/Thinklab-SJTU/Awesome-LLM4AD.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play
Abstract: Learning from unstructured and uncurated data has become the dominant
paradigm for generative approaches in language and vision. Such unstructured
and unguided behavior data, commonly known as play, is also easier to collect
in robotics but much more difficult to learn from due to its inherently
multimodal, noisy, and suboptimal nature. In this paper, we study this problem
of learning goal-directed skill policies from unstructured play data which is
labeled with language in hindsight. Specifically, we leverage advances in
diffusion models to learn a multi-task diffusion model to extract robotic
skills from play data. Using a conditional denoising diffusion process in the
space of states and actions, we can gracefully handle the complexity and
multimodality of play data and generate diverse and interesting robot
behaviors. To make diffusion models more useful for skill learning, we
encourage robotic agents to acquire a vocabulary of skills by introducing
discrete bottlenecks into the conditional behavior generation process. In our
experiments, we demonstrate the effectiveness of our approach across a wide
variety of environments in both simulation and the real world. Results
visualizations and videos at https://play-fusion.github.io

Output: Robotics


Instruction: What field is the article from?

Input:
Title: The Music Meta Ontology: a flexible semantic model for the interoperability of music metadata
Abstract: The semantic description of music metadata is a key requirement for the
creation of music datasets that can be aligned, integrated, and accessed for
information retrieval and knowledge discovery. It is nonetheless an open
challenge due to the complexity of musical concepts arising from different
genres, styles, and periods -- standing to benefit from a lingua franca to
accommodate various stakeholders (musicologists, librarians, data engineers,
etc.). To initiate this transition, we introduce the Music Meta ontology, a
rich and flexible semantic model to describe music metadata related to artists,
compositions, performances, recordings, and links. We follow eXtreme Design
methodologies and best practices for data engineering, to reflect the
perspectives and the requirements of various stakeholders into the design of
the model, while leveraging ontology design patterns and accounting for
provenance at different levels (claims, links). After presenting the main
features of Music Meta, we provide a first evaluation of the model, alignments
to other schema (Music Ontology, DOREMUS, Wikidata), and support for data
transformation.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: SuperHF: Supervised Iterative Learning from Human Feedback
Abstract: While large language models demonstrate remarkable capabilities, they often
present challenges in terms of safety, alignment with human values, and
stability during training. Here, we focus on two prevalent methods used to
align these models, Supervised Fine-Tuning (SFT) and Reinforcement Learning
from Human Feedback (RLHF). SFT is simple and robust, powering a host of
open-source models, while RLHF is a more sophisticated method used in top-tier
models like ChatGPT but also suffers from instability and susceptibility to
reward hacking. We propose a novel approach, Supervised Iterative Learning from
Human Feedback (SuperHF), which seeks to leverage the strengths of both
methods. Our hypothesis is two-fold: that the reward model used in RLHF is
critical for efficient data use and model generalization and that the use of
Proximal Policy Optimization (PPO) in RLHF may not be necessary and could
contribute to instability issues. SuperHF replaces PPO with a simple supervised
loss and a Kullback-Leibler (KL) divergence prior. It creates its own training
data by repeatedly sampling a batch of model outputs and filtering them through
the reward model in an online learning regime. We then break down the reward
optimization problem into three components: robustly optimizing the training
rewards themselves, preventing reward hacking-exploitation of the reward model
that degrades model performance-as measured by a novel METEOR similarity
metric, and maintaining good performance on downstream evaluations. Our
experimental results show SuperHF exceeds PPO-based RLHF on the training
objective, easily and favorably trades off high reward with low reward hacking,
improves downstream calibration, and performs the same on our GPT-4 based
qualitative evaluation scheme all the while being significantly simpler to
implement, highlighting SuperHF's potential as a competitive language model
alignment technique.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models
Abstract: Pre-trained and frozen large language models (LLMs) can effectively map
simple scene rearrangement instructions to programs over a robot's visuomotor
functions through appropriate few-shot example prompting. To parse open-domain
natural language and adapt to a user's idiosyncratic procedures, not known
during prompt engineering time, fixed prompts fall short. In this paper, we
introduce HELPER, an embodied agent equipped with an external memory of
language-program pairs that parses free-form human-robot dialogue into action
programs through retrieval-augmented LLM prompting: relevant memories are
retrieved based on the current dialogue, instruction, correction, or VLM
description, and used as in-context prompt examples for LLM querying. The
memory is expanded during deployment to include pairs of user's language and
action plans, to assist future inferences and personalize them to the user's
language and routines. HELPER sets a new state-of-the-art in the TEACh
benchmark in both Execution from Dialog History (EDH) and Trajectory from
Dialogue (TfD), with a 1.7x improvement over the previous state-of-the-art for
TfD. Our models, code, and video results can be found in our project's website:
https://helper-agent-llm.github.io.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Towards Evaluating AI Systems for Moral Status Using Self-Reports
Abstract: As AI systems become more advanced and widely deployed, there will likely be
increasing debate over whether AI systems could have conscious experiences,
desires, or other states of potential moral significance. It is important to
inform these discussions with empirical evidence to the extent possible. We
argue that under the right circumstances, self-reports, or an AI system's
statements about its own internal states, could provide an avenue for
investigating whether AI systems have states of moral significance.
Self-reports are the main way such states are assessed in humans ("Are you in
pain?"), but self-reports from current systems like large language models are
spurious for many reasons (e.g. often just reflecting what humans would say).
To make self-reports more appropriate for this purpose, we propose to train
models to answer many kinds of questions about themselves with known answers,
while avoiding or limiting training incentives that bias self-reports. The hope
of this approach is that models will develop introspection-like capabilities,
and that these capabilities will generalize to questions about states of moral
significance. We then propose methods for assessing the extent to which these
techniques have succeeded: evaluating self-report consistency across contexts
and between similar models, measuring the confidence and resilience of models'
self-reports, and using interpretability to corroborate self-reports. We also
discuss challenges for our approach, from philosophical difficulties in
interpreting self-reports to technical reasons why our proposal might fail. We
hope our discussion inspires philosophers and AI researchers to criticize and
improve our proposed methodology, as well as to run experiments to test whether
self-reports can be made reliable enough to provide information about states of
moral significance.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Attention Mechanism for Lithium-Ion Battery Lifespan Prediction: Temporal and Cyclic Attention
Abstract: Accurately predicting lithium-ion batteries (LIBs) lifespan is pivotal for
optimizing usage and preventing accidents. Previous approaches often relied on
inputs challenging to measure in real-time, and failed to capture intra- and
inter-cycle data patterns simultaneously. Our study employ attention mechanisms
(AM) to develop data-driven models predicting LIB lifespan using easily
measurable inputs. Developed model integrates recurrent neural network and
convolutional neural network, featuring two types of AMs: temporal attention
(TA) and cyclic attention (CA). TA identifies important time steps within each
cycle, CA strives to capture key features of inter-cycle correlations through
self-attention (SA). We apply the developed model to publicly available data
consisting of three batches of cycling modes. TA scores highlight the rest
phase as a key characteristic to distinguish different batches. By leveraging
CA scores, we decreased the input dimension from 100 cycles to 50 and 30 cycles
with single- and multi-head attention.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Large Language Models for Travel Behavior Prediction
Abstract: Travel behavior prediction is a fundamental task in transportation demand
management. The conventional methods for travel behavior prediction rely on
numerical data to construct mathematical models and calibrate model parameters
to represent human preferences. Recent advancement in large language models
(LLMs) has shown great reasoning abilities to solve complex problems. In this
study, we propose to use LLMs to predict travel behavior with prompt
engineering without data-based parameter learning. Specifically, we carefully
design our prompts that include 1) task description, 2) travel characteristics,
3) individual attributes, and 4) guides of thinking with domain knowledge, and
ask the LLMs to predict an individual's travel behavior and explain the
results. We select the travel mode choice task as a case study. Results show
that, though no training samples are provided, LLM-based predictions have
competitive accuracy and F1-score as canonical supervised learning methods such
as multinomial logit, random forest, and neural networks. LLMs can also output
reasons that support their prediction. However, though in most of the cases,
the output explanations are reasonable, we still observe cases that violate
logic or with hallucinations.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning
Abstract: Generally, Reinforcement Learning (RL) agent updates its policy by
repetitively interacting with the environment, contingent on the received
rewards to observed states and undertaken actions. However, the environmental
disturbance, commonly leading to noisy observations (e.g., rewards and states),
could significantly shape the performance of agent. Furthermore, the learning
performance of Multi-Agent Reinforcement Learning (MARL) is more susceptible to
noise due to the interference among intelligent agents. Therefore, it becomes
imperative to revolutionize the design of MARL, so as to capably ameliorate the
annoying impact of noisy rewards. In this paper, we propose a novel
decomposition-based multi-agent distributional RL method by approximating the
globally shared noisy reward by a Gaussian mixture model (GMM) and decomposing
it into the combination of individual distributional local rewards, with which
each agent can be updated locally through distributional RL. Moreover, a
diffusion model (DM) is leveraged for reward generation in order to mitigate
the issue of costly interaction expenditure for learning distributions.
Furthermore, the optimality of the distribution decomposition is theoretically
validated, while the design of loss function is carefully calibrated to avoid
the decomposition ambiguity. We also verify the effectiveness of the proposed
method through extensive simulation experiments with noisy rewards. Besides,
different risk-sensitive policies are evaluated in order to demonstrate the
superiority of distributional RL in different MARL tasks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Evaluating GPT-4's Vision Capabilities on Brazilian University Admission Exams
Abstract: Recent advancements in language models have showcased human-comparable
performance in academic entrance exams. However, existing studies often
overlook questions that require the integration of visual comprehension, thus
compromising the full spectrum and complexity inherent in real-world scenarios.
To address this gap, we present a comprehensive framework to evaluate language
models on entrance exams, which incorporates both textual and visual elements.
We evaluate the two most recent editions of Exame Nacional do Ensino M\'edio
(ENEM), the main standardized entrance examination adopted by Brazilian
universities. Our study not only reaffirms the capabilities of GPT-4 as the
state of the art for handling complex multidisciplinary questions, but also
pioneers in offering a realistic assessment of multimodal language models on
Portuguese examinations. One of the highlights is that text captions
transcribing visual content outperform the direct use of images, suggesting
that the vision model has room for improvement. Yet, despite improvements
afforded by images or captions, mathematical questions remain a challenge for
these state-of-the-art models. The code and data used on experiments are
available at https://github.com/piresramon/gpt-4-enem.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Adaptive Multi-Modality Prompt Learning
Abstract: Although current prompt learning methods have successfully been designed to
effectively reuse the large pre-trained models without fine-tuning their large
number of parameters, they still have limitations to be addressed, i.e.,
without considering the adverse impact of meaningless patches in every image
and without simultaneously considering in-sample generalization and
out-of-sample generalization. In this paper, we propose an adaptive
multi-modality prompt learning to address the above issues. To do this, we
employ previous text prompt learning and propose a new image prompt learning.
The image prompt learning achieves in-sample and out-of-sample generalization,
by first masking meaningless patches and then padding them with the learnable
parameters and the information from texts. Moreover, each of the prompts
provides auxiliary information to each other, further strengthening these two
kinds of generalization. Experimental results on real datasets demonstrate that
our method outperforms SOTA methods, in terms of different downstream tasks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling
Abstract: This study investigates the efficacy of Large Language Models (LLMs) in
interactive language therapy for high-functioning autistic adolescents. With
the rapid advancement of artificial intelligence, particularly in natural
language processing, LLMs present a novel opportunity to augment traditional
psychological counseling methods. This research primarily focuses on evaluating
the LLM's ability to engage in empathetic, adaptable, and contextually
appropriate interactions within a therapeutic setting. A comprehensive
evaluation was conducted by a panel of clinical psychologists and psychiatrists
using a specially developed scorecard. The assessment covered various aspects
of the LLM's performance, including empathy, communication skills,
adaptability, engagement, and the ability to establish a therapeutic alliance.
The study avoided direct testing with patients, prioritizing privacy and
ethical considerations, and instead relied on simulated scenarios to gauge the
LLM's effectiveness. The results indicate that LLMs hold significant promise as
supportive tools in therapy, demonstrating strengths in empathetic engagement
and adaptability in conversation. However, challenges in achieving the depth of
personalization and emotional understanding characteristic of human therapists
were noted. The study also highlights the importance of ethical considerations
in the application of AI in therapeutic contexts. This research provides
valuable insights into the potential and limitations of using LLMs in
psychological counseling for autistic adolescents. It lays the groundwork for
future explorations into AI's role in mental health care, emphasizing the need
for ongoing development to enhance the capabilities of these models in
therapeutic settings.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Evolutionary Tabletop Game Design: A Case Study in the Risk Game
Abstract: Creating and evaluating games manually is an arduous and laborious task.
Procedural content generation can aid by creating game artifacts, but usually
not an entire game. Evolutionary game design, which combines evolutionary
algorithms with automated playtesting, has been used to create novel board
games with simple equipment; however, the original approach does not include
complex tabletop games with dice, cards, and maps. This work proposes an
extension of the approach for tabletop games, evaluating the process by
generating variants of Risk, a military strategy game where players must
conquer map territories to win. We achieved this using a genetic algorithm to
evolve the chosen parameters, as well as a rules-based agent to test the games
and a variety of quality criteria to evaluate the new variations generated. Our
results show the creation of new variations of the original game with smaller
maps, resulting in shorter matches. Also, the variants produce more balanced
matches, maintaining the usual drama. We also identified limitations in the
process, where, in many cases, where the objective function was correctly
pursued, but the generated games were nearly trivial. This work paves the way
towards promising research regarding the use of evolutionary game design beyond
classic board games.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: FaceStudio: Put Your Face Everywhere in Seconds
Abstract: This study investigates identity-preserving image synthesis, an intriguing
task in image generation that seeks to maintain a subject's identity while
adding a personalized, stylistic touch. Traditional methods, such as Textual
Inversion and DreamBooth, have made strides in custom image creation, but they
come with significant drawbacks. These include the need for extensive resources
and time for fine-tuning, as well as the requirement for multiple reference
images. To overcome these challenges, our research introduces a novel approach
to identity-preserving synthesis, with a particular focus on human images. Our
model leverages a direct feed-forward mechanism, circumventing the need for
intensive fine-tuning, thereby facilitating quick and efficient image
generation. Central to our innovation is a hybrid guidance framework, which
combines stylized images, facial images, and textual prompts to guide the image
generation process. This unique combination enables our model to produce a
variety of applications, such as artistic portraits and identity-blended
images. Our experimental results, including both qualitative and quantitative
evaluations, demonstrate the superiority of our method over existing baseline
models and previous works, particularly in its remarkable efficiency and
ability to preserve the subject's identity with high fidelity.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Attention-based Models for Snow-Water Equivalent Prediction
Abstract: Snow Water-Equivalent (SWE) -- the amount of water available if snowpack is
melted -- is a key decision variable used by water management agencies to make
irrigation, flood control, power generation and drought management decisions.
SWE values vary spatiotemporally -- affected by weather, topography and other
environmental factors. While daily SWE can be measured by Snow Telemetry
(SNOTEL) stations with requisite instrumentation, such stations are spatially
sparse requiring interpolation techniques to create spatiotemporally complete
data. While recent efforts have explored machine learning (ML) for SWE
prediction, a number of recent ML advances have yet to be considered. The main
contribution of this paper is to explore one such ML advance, attention
mechanisms, for SWE prediction. Our hypothesis is that attention has a unique
ability to capture and exploit correlations that may exist across locations or
the temporal spectrum (or both). We present a generic attention-based modeling
framework for SWE prediction and adapt it to capture spatial attention and
temporal attention. Our experimental results on 323 SNOTEL stations in the
Western U.S. demonstrate that our attention-based models outperform other
machine learning approaches. We also provide key results highlighting the
differences between spatial and temporal attention in this context and a
roadmap toward deployment for generating spatially-complete SWE maps.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction
Abstract: Appropriate Trust in Artificial Intelligence (AI) systems has rapidly become
an important area of focus for both researchers and practitioners. Various
approaches have been used to achieve it, such as confidence scores,
explanations, trustworthiness cues, or uncertainty communication. However, a
comprehensive understanding of the field is lacking due to the diversity of
perspectives arising from various backgrounds that influence it and the lack of
a single definition for appropriate trust. To investigate this topic, this
paper presents a systematic review to identify current practices in building
appropriate trust, different ways to measure it, types of tasks used, and
potential challenges associated with it. We also propose a Belief, Intentions,
and Actions (BIA) mapping to study commonalities and differences in the
concepts related to appropriate trust by (a) describing the existing
disagreements on defining appropriate trust, and (b) providing an overview of
the concepts and definitions related to appropriate trust in AI from the
existing literature. Finally, the challenges identified in studying appropriate
trust are discussed, and observations are summarized as current trends,
potential gaps, and research opportunities for future work. Overall, the paper
provides insights into the complex concept of appropriate trust in human-AI
interaction and presents research opportunities to advance our understanding on
this topic.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Meta-survey on outlier and anomaly detection
Abstract: The impact of outliers and anomalies on model estimation and data processing
is of paramount importance, as evidenced by the extensive body of research
spanning various fields over several decades: thousands of research papers have
been published on the subject. As a consequence, numerous reviews, surveys, and
textbooks have sought to summarize the existing literature, encompassing a wide
range of methods from both the statistical and data mining communities. While
these endeavors to organize and summarize the research are invaluable, they
face inherent challenges due to the pervasive nature of outliers and anomalies
in all data-intensive applications, irrespective of the specific application
field or scientific discipline. As a result, the resulting collection of papers
remains voluminous and somewhat heterogeneous. To address the need for
knowledge organization in this domain, this paper implements the first
systematic meta-survey of general surveys and reviews on outlier and anomaly
detection. Employing a classical systematic survey approach, the study collects
nearly 500 papers using two specialized scientific search engines. From this
comprehensive collection, a subset of 56 papers that claim to be general
surveys on outlier detection is selected using a snowball search technique to
enhance field coverage. A meticulous quality assessment phase further refines
the selection to a subset of 25 high-quality general surveys. Using this
curated collection, the paper investigates the evolution of the outlier
detection field over a 20-year period, revealing emerging themes and methods.
Furthermore, an analysis of the surveys sheds light on the survey writing
practices adopted by scholars from different communities who have contributed
to this field. Finally, the paper delves into several topics where consensus
has emerged from the literature. These include taxonomies of outlier types,
challenges posed by high-dimensional data, the importance of anomaly scores,
the impact of learning conditions, difficulties in benchmarking, and the
significance of neural networks. Non-consensual aspects are also discussed,
particularly the distinction between local and global outliers and the
challenges in organizing detection methods into meaningful taxonomies.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Large Language Models as Generalizable Policies for Embodied Tasks
Abstract: We show that large language models (LLMs) can be adapted to be generalizable
policies for embodied visual tasks. Our approach, called Large LAnguage model
Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take
as input text instructions and visual egocentric observations and output
actions directly in the environment. Using reinforcement learning, we train
LLaRP to see and act solely through environmental interactions. We show that
LLaRP is robust to complex paraphrasings of task instructions and can
generalize to new tasks that require novel optimal behavior. In particular, on
1,000 unseen tasks it achieves 42% success rate, 1.7x the success rate of other
common learned baselines or zero-shot applications of LLMs. Finally, to aid the
community in studying language conditioned, massively multi-task, embodied AI
problems we release a novel benchmark, Language Rearrangement, consisting of
150,000 training and 1,000 testing tasks for language-conditioned
rearrangement. Video examples of LLaRP in unseen Language Rearrangement
instructions are at https://llm-rl.github.io.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems
Abstract: A Collaborative Artificial Intelligence System (CAIS) is a cyber-physical
system that learns actions in collaboration with humans in a shared environment
to achieve a common goal. In particular, a CAIS is equipped with an AI model to
support the decision-making process of this collaboration. When an event
degrades the performance of CAIS (i.e., a disruptive event), this
decision-making process may be hampered or even stopped. Thus, it is of
paramount importance to monitor the learning of the AI model, and eventually
support its decision-making process in such circumstances. This paper
introduces a new methodology to automatically support the decision-making
process in CAIS when the system experiences performance degradation after a
disruptive event. To this aim, we develop a framework that consists of three
components: one manages or simulates CAIS's environment and disruptive events,
the second automates the decision-making process, and the third provides a
visual analysis of CAIS behavior. Overall, our framework automatically monitors
the decision-making process, intervenes whenever a performance degradation
occurs, and recommends the next action. We demonstrate our framework by
implementing an example with a real-world collaborative robot, where the
framework recommends the next action that balances between minimizing the
recovery time (i.e., resilience), and minimizing the energy adverse effects
(i.e., greenness).

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: M2ConceptBase: A Fine-grained Aligned Multi-modal Conceptual Knowledge Base
Abstract: Large multi-modal models (LMMs) have demonstrated promising intelligence
owing to the rapid development of pre-training techniques. However, their
fine-grained cross-modal alignment ability is constrained by the coarse
alignment in image-text pairs. This limitation hinders awareness of
fine-grained concepts, resulting in sub-optimal performance. In this paper, we
propose a multi-modal conceptual knowledge base, named M2ConceptBase, which
aims to provide fine-grained alignment between images and concepts.
Specifically, M2ConceptBase models concepts as nodes, associating each with
relevant images and detailed text, thereby enhancing LMMs' cross-modal
alignment with rich conceptual knowledge. To collect concept-image and
concept-description alignments, we propose a context-aware multi-modal symbol
grounding approach that considers context information in existing large-scale
image-text pairs with respect to each concept. A cutting-edge large language
model supplements descriptions for concepts not grounded via our symbol
grounding approach. Finally, our M2ConceptBase contains more than 951K images
and 152K concepts, each associating with an average of 6.27 images and a single
detailed description. We conduct experiments on the OK-VQA task, demonstrating
that our M2ConceptBase facilitates the model in achieving state-of-the-art
performance. Moreover, we construct a comprehensive benchmark to evaluate the
concept understanding of LMMs and show that M2ConceptBase could effectively
improve LMMs' concept understanding and cross-modal alignment abilities.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Web3 Meets AI Marketplace: Exploring Opportunities, Analyzing Challenges, and Suggesting Solutions
Abstract: Web3 and AI have been among the most discussed fields over the recent years,
with substantial hype surrounding each field's potential to transform the world
as we know it. However, as the hype settles, it's evident that neither AI nor
Web3 can address all challenges independently. Consequently, the intersection
of AI and Web3 is gaining increased attention, emerging as a new field with the
potential to address the limitations of each. In this article, we will focus on
the integration of web3 and the AI marketplace, where AI services and products
can be provided in a decentralized manner (DeAI). A comprehensive review is
provided by summarizing the opportunities and challenges on this topic.
Additionally, we offer analyses and solutions to address these challenges.
We've developed a framework that lets users pay with any kind of cryptocurrency
to get AI services. Additionally, they can also enjoy AI services for free on
our platform by simply locking up their assets temporarily in the protocol.
This unique approach is a first in the industry. Before this, offering free AI
services in the web3 community wasn't possible. Our solution opens up exciting
opportunities for the AI marketplace in the web3 space to grow and be widely
adopted.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Consistency Models for Scalable and Fast Simulation-Based Inference
Abstract: Simulation-based inference (SBI) is constantly in search of more expressive
algorithms for accurately inferring the parameters of complex models from noisy
data. We present consistency models for neural posterior estimation (CMPE), a
new free-form conditional sampler for scalable, fast, and amortized SBI with
generative neural networks. CMPE combines the advantages of normalizing flows
and flow matching methods into a single generative architecture: It essentially
distills a continuous probability flow and enables rapid few-shot inference
with an unconstrained architecture that can be tailored to the structure of the
estimation problem. Our empirical evaluation demonstrates that CMPE not only
outperforms current state-of-the-art algorithms on three hard low-dimensional
problems, but also achieves competitive performance in a high-dimensional
Bayesian denoising experiment and in estimating a computationally demanding
multi-scale model of tumor spheroid growth.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection
Abstract: Network intrusion detection (NID) systems which leverage machine learning
have been shown to have strong performance in practice when used to detect
malicious network traffic. Decision trees in particular offer a strong balance
between performance and simplicity, but require users of NID systems to have
background knowledge in machine learning to interpret. In addition, they are
unable to provide additional outside information as to why certain features may
be important for classification.
  In this work, we explore the use of large language models (LLMs) to provide
explanations and additional background knowledge for decision tree NID systems.
Further, we introduce a new human evaluation framework for decision tree
explanations, which leverages automatically generated quiz questions that
measure human evaluators' understanding of decision tree inference. Finally, we
show LLM generated decision tree explanations correlate highly with human
ratings of readability, quality, and use of background knowledge while
simultaneously providing better understanding of decision boundaries.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach
Abstract: Video Anomaly Detection (VAD) is an open-set recognition task, which is
usually formulated as a one-class classification (OCC) problem, where training
data is comprised of videos with normal instances while test data contains both
normal and anomalous instances. Recent works have investigated the creation of
pseudo-anomalies (PAs) using only the normal data and making strong assumptions
about real-world anomalies with regards to abnormality of objects and speed of
motion to inject prior information about anomalies in an autoencoder (AE) based
reconstruction model during training. This work proposes a novel method for
generating generic spatio-temporal PAs by inpainting a masked out region of an
image using a pre-trained Latent Diffusion Model and further perturbing the
optical flow using mixup to emulate spatio-temporal distortions in the data. In
addition, we present a simple unified framework to detect real-world anomalies
under the OCC setting by learning three types of anomaly indicators, namely
reconstruction quality, temporal irregularity and semantic inconsistency.
Extensive experiments on four VAD benchmark datasets namely Ped2, Avenue,
ShanghaiTech and UBnormal demonstrate that our method performs on par with
other existing state-of-the-art PAs generation and reconstruction based methods
under the OCC setting. Our analysis also examines the transferability and
generalisation of PAs across these datasets, offering valuable insights by
identifying real-world anomalies through PAs.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Instructed Language Models with Retrievers Are Powerful Entity Linkers
Abstract: Generative approaches powered by large language models (LLMs) have
demonstrated emergent abilities in tasks that require complex reasoning
abilities. Yet the generative nature still makes the generated content suffer
from hallucinations, thus unsuitable for entity-centric tasks like entity
linking (EL) requiring precise entity predictions over a large knowledge base.
We present Instructed Generative Entity Linker (INSGENEL), the first approach
that enables casual language models to perform entity linking over knowledge
bases. Several methods to equip language models with EL capability were
proposed in this work, including (i) a sequence-to-sequence training EL
objective with instruction-tuning, (ii) a novel generative EL framework based
on a light-weight potential mention retriever that frees the model from heavy
and non-parallelizable decoding, achieving 4$\times$ speedup without compromise
on linking metrics. INSGENEL outperforms previous generative alternatives with
+6.8 F1 points gain on average, also with a huge advantage in training data
efficiency and training compute consumption. In addition, our skillfully
engineered in-context learning (ICL) framework for EL still lags behind
INSGENEL significantly, reaffirming that the EL task remains a persistent
hurdle for general LLMs.

Output: Computational Linguistics