Scheduled Commit

data/2410.09019.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.09019", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Boosting Healthcare LLMs Through Retrieved Context](https://huggingface.co/papers/2409.15127) (2024)\n* [Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs](https://huggingface.co/papers/2409.14988) (2024)\n* [Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models](https://huggingface.co/papers/2409.00084) (2024)\n* [MoDEM: Mixture of Domain Expert Models](https://huggingface.co/papers/2410.07490) (2024)\n* [Towards Evaluating and Building Versatile Large Language Models for Medicine](https://huggingface.co/papers/2408.12547) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.09347.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.09347", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Show-o: One Single Transformer to Unify Multimodal Understanding and Generation](https://huggingface.co/papers/2408.12528) (2024)\n* [VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation](https://huggingface.co/papers/2409.04429) (2024)\n* [DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation](https://huggingface.co/papers/2410.08159) (2024)\n* [Diversity-Rewarded CFG Distillation](https://huggingface.co/papers/2410.06084) (2024)\n* [T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design](https://huggingface.co/papers/2410.05677) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.09426.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.09426", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs](https://huggingface.co/papers/2410.05265) (2024)\n* [Rotated Runtime Smooth: Training-Free Activation Smoother for accurate INT4 inference](https://huggingface.co/papers/2409.20361) (2024)\n* [CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression](https://huggingface.co/papers/2410.07505) (2024)\n* [MobileQuant: Mobile-friendly Quantization for On-device Language Models](https://huggingface.co/papers/2408.13933) (2024)\n* [VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models](https://huggingface.co/papers/2409.17066) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.10210.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.10210", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [SAG: Style-Aligned Article Generation via Model Collaboration](https://huggingface.co/papers/2410.03137) (2024)\n* [Self-Data Distillation for Recovering Quality in Pruned Large Language Models](https://huggingface.co/papers/2410.09982) (2024)\n* [How to Train Long-Context Language Models (Effectively)](https://huggingface.co/papers/2410.02660) (2024)\n* [SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe](https://huggingface.co/papers/2410.05248) (2024)\n* [Self-Boosting Large Language Models with Synthetic Preference Data](https://huggingface.co/papers/2410.06961) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.11842.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.11842", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts](https://huggingface.co/papers/2410.07348) (2024)\n* [Differential Transformer](https://huggingface.co/papers/2410.05258) (2024)\n* [CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling](https://huggingface.co/papers/2409.19291) (2024)\n* [SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture](https://huggingface.co/papers/2410.07739) (2024)\n* [Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection](https://huggingface.co/papers/2410.02330) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.12183.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.12183", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter](https://huggingface.co/papers/2410.07854) (2024)\n* [Cascade Prompt Learning for Vision-Language Model Adaptation](https://huggingface.co/papers/2409.17805) (2024)\n* [Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning](https://huggingface.co/papers/2408.16486) (2024)\n* [Multi-Modal Adapter for Vision-Language Models](https://huggingface.co/papers/2409.02958) (2024)\n* [LOBG:Less Overfitting for Better Generalization in Vision-Language Model](https://huggingface.co/papers/2410.10247) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.12705.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.12705", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs](https://huggingface.co/papers/2409.11404) (2024)\n* [Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect](https://huggingface.co/papers/2409.17912) (2024)\n* [L3Cube-IndicQuest: A Benchmark Questing Answering Dataset for Evaluating Knowledge of LLMs in Indic Context](https://huggingface.co/papers/2409.08706) (2024)\n* [Brotherhood at WMT 2024: Leveraging LLM-Generated Contextual Conversations for Cross-Lingual Image Captioning](https://huggingface.co/papers/2409.15052) (2024)\n* [XTRUST: On the Multilingual Trustworthiness of Large Language Models](https://huggingface.co/papers/2409.15762) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.12771.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.12771", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Generative AI for Discovering Porous Oxide Materials for Next-Generation Energy Storage](https://huggingface.co/papers/2410.06433) (2024)\n* [VQCrystal: Leveraging Vector Quantization for Discovery of Stable Crystal Structures](https://huggingface.co/papers/2409.06191) (2024)\n* [Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks](https://huggingface.co/papers/2409.13851) (2024)\n* [Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials](https://huggingface.co/papers/2409.00957) (2024)\n* [Northeast Materials Database (NEMAD): Enabling Discovery of High Transition Temperature Magnetic Compounds](https://huggingface.co/papers/2409.15675) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.12781.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.12781", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [RelitLRM: Generative Relightable Radiance for Large Reconstruction Models](https://huggingface.co/papers/2410.06231) (2024)\n* [G3R: Gradient Guided Generalizable Reconstruction](https://huggingface.co/papers/2409.19405) (2024)\n* [TranSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Transformers](https://huggingface.co/papers/2408.13770) (2024)\n* [DrivingForward: Feed-forward 3D Gaussian Splatting for Driving Scene Reconstruction from Flexible Surround-view Input](https://huggingface.co/papers/2409.12753) (2024)\n* [Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs](https://huggingface.co/papers/2408.13912) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.12784.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.12784", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Direct Judgement Preference Optimization](https://huggingface.co/papers/2409.14664) (2024)\n* [RevisEval: Improving LLM-as-a-Judge via Response-Adapted References](https://huggingface.co/papers/2410.05193) (2024)\n* [LLM-as-a-Judge & Reward Model: What They Can and Cannot Do](https://huggingface.co/papers/2409.11239) (2024)\n* [Self-rationalization improves LLM as a fine-grained judge](https://huggingface.co/papers/2410.05495) (2024)\n* [RMB: Comprehensively Benchmarking Reward Models in LLM Alignment](https://huggingface.co/papers/2410.09893) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.12957.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.12957", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos](https://huggingface.co/papers/2409.07450) (2024)\n* [Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis](https://huggingface.co/papers/2409.06135) (2024)\n* [Temporally Aligned Audio for Video with Autoregression](https://huggingface.co/papers/2409.13689) (2024)\n* [STA-V2A: Video-to-Audio Generation with Semantic and Temporal Alignment](https://huggingface.co/papers/2409.08601) (2024)\n* [Rhythmic Foley: A Framework For Seamless Audio-Visual Alignment In Video-to-Audio Synthesis](https://huggingface.co/papers/2409.08628) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13060.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13060", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models](https://huggingface.co/papers/2410.09637) (2024)\n* [Power-Softmax: Towards Secure LLM Inference over Encrypted Data](https://huggingface.co/papers/2410.09457) (2024)\n* [Encryption-Friendly LLM Architecture](https://huggingface.co/papers/2410.02486) (2024)\n* [Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model](https://huggingface.co/papers/2408.10764) (2024)\n* [FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction](https://huggingface.co/papers/2410.12513) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13085.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13085", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models](https://huggingface.co/papers/2408.13808) (2024)\n* [LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic Pathologies](https://huggingface.co/papers/2410.04749) (2024)\n* [ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models](https://huggingface.co/papers/2409.09318) (2024)\n* [OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography](https://huggingface.co/papers/2409.09052) (2024)\n* [TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions](https://huggingface.co/papers/2410.04107) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13198.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13198", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Large Language Model Should Understand Pinyin for Chinese ASR Error Correction](https://huggingface.co/papers/2409.13262) (2024)\n* [Retrieval Augmented Correction of Named Entity Speech Recognition Errors](https://huggingface.co/papers/2409.06062) (2024)\n* [MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues](https://huggingface.co/papers/2408.14418) (2024)\n* [Full-text Error Correction for Chinese Speech Recognition with Large Language Model](https://huggingface.co/papers/2409.07790) (2024)\n* [LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation](https://huggingface.co/papers/2409.08597) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13268.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13268", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data](https://huggingface.co/papers/2409.20007) (2024)\n* [Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions](https://huggingface.co/papers/2409.08596) (2024)\n* [WHISMA: A Speech-LLM to Perform Zero-shot Spoken Language Understanding](https://huggingface.co/papers/2408.16423) (2024)\n* [SpeechCraft: A Fine-grained Expressive Speech Dataset with Natural Language Description](https://huggingface.co/papers/2408.13608) (2024)\n* [EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions](https://huggingface.co/papers/2409.18042) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13293.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13293", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models](https://huggingface.co/papers/2410.08068) (2024)\n* [Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving](https://huggingface.co/papers/2408.14494) (2024)\n* [Logic Contrastive Reasoning with Lightweight Large Language Model for Math Word Problems](https://huggingface.co/papers/2409.00131) (2024)\n* [Unlocking Structured Thinking in Language Models with Cognitive Prompting](https://huggingface.co/papers/2410.02953) (2024)\n* [LLaMa-SciQ: An Educational Chatbot for Answering Science MCQ](https://huggingface.co/papers/2409.16779) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13334.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13334", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Jailbreak Antidote: Runtime Safety-Utility Balance via Sparse Representation Adjustment in Large Language Models](https://huggingface.co/papers/2410.02298) (2024)\n* [Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations](https://huggingface.co/papers/2410.09097) (2024)\n* [You Know What I'm Saying: Jailbreak Attack via Implicit Reference](https://huggingface.co/papers/2410.03857) (2024)\n* [Harnessing Task Overload for Scalable Jailbreak Attacks on Large Language Models](https://huggingface.co/papers/2410.04190) (2024)\n* [Effective and Evasive Fuzz Testing-Driven Jailbreaking Attacks against LLMs](https://huggingface.co/papers/2409.14866) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13360.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13360", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [RoRA-VLM: Robust Retrieval-Augmented Vision Language Models](https://huggingface.co/papers/2410.08876) (2024)\n* [SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information](https://huggingface.co/papers/2409.14083) (2024)\n* [MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models](https://huggingface.co/papers/2410.08182) (2024)\n* [Self-adaptive Multimodal Retrieval-Augmented Generation](https://huggingface.co/papers/2410.11321) (2024)\n* [VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents](https://huggingface.co/papers/2410.10594) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13618.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13618", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values](https://huggingface.co/papers/2409.05926) (2024)\n* [LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models](https://huggingface.co/papers/2410.11551) (2024)\n* [CoRA: Optimizing Low-Rank Adaptation with Common Subspace of Large Language Models](https://huggingface.co/papers/2409.02119) (2024)\n* [One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation](https://huggingface.co/papers/2410.07170) (2024)\n* [HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation](https://huggingface.co/papers/2409.13501) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13639.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13639", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Steering Large Language Models between Code Execution and Textual Reasoning](https://huggingface.co/papers/2410.03524) (2024)\n* [Auto-Evolve: Enhancing Large Language Model's Performance via Self-Reasoning Framework](https://huggingface.co/papers/2410.06328) (2024)\n* [Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification](https://huggingface.co/papers/2410.05318) (2024)\n* [BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search](https://huggingface.co/papers/2409.17972) (2024)\n* [Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models](https://huggingface.co/papers/2410.07985) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13720.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13720", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [LoVA: Long-form Video-to-Audio Generation](https://huggingface.co/papers/2409.15157) (2024)\n* [SONIQUE: Video Background Music Generation Using Unpaired Audio-Visual Data](https://huggingface.co/papers/2410.03879) (2024)\n* [Exploring Efficient Foundational Multi-modal Models for Video Summarization](https://huggingface.co/papers/2410.07405) (2024)\n* [Loong: Generating Minute-level Long Videos with Autoregressive Language Models](https://huggingface.co/papers/2410.02757) (2024)\n* [The Dawn of Video Generation: Preliminary Explorations with SORA-like Models](https://huggingface.co/papers/2410.05227) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13754.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13754", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [OmnixR: Evaluating Omni-modality Language Models on Reasoning across Modalities](https://huggingface.co/papers/2410.12219) (2024)\n* [MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks](https://huggingface.co/papers/2410.10563) (2024)\n* [LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models](https://huggingface.co/papers/2410.09732) (2024)\n* [Parameter Choice and Neuro-Symbolic Approaches for Deep Domain-Invariant Learning](https://huggingface.co/papers/2410.06235) (2024)\n* [Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization](https://huggingface.co/papers/2409.18433) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13757.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13757", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Agent S: An Open Agentic Framework that Uses Computers Like a Human](https://huggingface.co/papers/2410.08164) (2024)\n* [WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration](https://huggingface.co/papers/2408.15978) (2024)\n* [Dynamic Planning for LLM-based Graphical User Interface Automation](https://huggingface.co/papers/2410.00467) (2024)\n* [Turn Every Application into an Agent: Towards Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents](https://huggingface.co/papers/2409.17140) (2024)\n* [AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environment](https://huggingface.co/papers/2409.17655) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13785.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13785", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [SeRA: Self-Reviewing and Alignment of Large Language Models using Implicit Reward Margins](https://huggingface.co/papers/2410.09362) (2024)\n* [Negative-Prompt-driven Alignment for Generative Language Model](https://huggingface.co/papers/2410.12194) (2024)\n* [REAL: Response Embedding-based Alignment for LLMs](https://huggingface.co/papers/2409.17169) (2024)\n* [Towards a Unified View of Preference Learning for Large Language Models: A Survey](https://huggingface.co/papers/2409.02795) (2024)\n* [Less for More: Enhancing Preference Learning in Generative Language Models with Automated Self-Curation of Training Corpora](https://huggingface.co/papers/2408.12799) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13804.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13804", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [ELICIT: LLM Augmentation via External In-Context Capability](https://huggingface.co/papers/2410.09343) (2024)\n* [Making Text Embedders Few-Shot Learners](https://huggingface.co/papers/2409.15700) (2024)\n* [DemoShapley: Valuation of Demonstrations for In-Context Learning](https://huggingface.co/papers/2410.07523) (2024)\n* [In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks](https://huggingface.co/papers/2410.01548) (2024)\n* [Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement](https://huggingface.co/papers/2409.11378) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13824.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13824", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://huggingface.co/papers/2410.05243) (2024)\n* [Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks](https://huggingface.co/papers/2410.01744) (2024)\n* [Building and better understanding vision-language models: insights and future directions](https://huggingface.co/papers/2408.12637) (2024)\n* [WebQuest: A Benchmark for Multimodal QA on Web Page Sequences](https://huggingface.co/papers/2409.13711) (2024)\n* [MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models](https://huggingface.co/papers/2410.09733) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13830.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13830", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [TrackGo: A Flexible and Efficient Method for Controllable Video Generation](https://huggingface.co/papers/2408.11475) (2024)\n* [CustomCrafter: Customized Video Generation with Preserving Motion and Concept Composition Abilities](https://huggingface.co/papers/2408.13239) (2024)\n* [OmniBooth: Learning Latent Control for Image Synthesis with Multi-modal Instruction](https://huggingface.co/papers/2410.04932) (2024)\n* [AMG: Avatar Motion Guided Video Generation](https://huggingface.co/papers/2409.01502) (2024)\n* [FreeMask: Rethinking the Importance of Attention Masks for Zero-Shot Video Editing](https://huggingface.co/papers/2409.20500) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13832.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13832", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation](https://huggingface.co/papers/2408.15239) (2024)\n* [ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis](https://huggingface.co/papers/2409.02048) (2024)\n* [Video Diffusion Models are Strong Video Inpainter](https://huggingface.co/papers/2408.11402) (2024)\n* [Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention](https://huggingface.co/papers/2410.10774) (2024)\n* [Pano2Room: Novel View Synthesis from a Single Indoor Panorama](https://huggingface.co/papers/2408.11413) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13841.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13841", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models](https://huggingface.co/papers/2410.09344) (2024)\n* [NEAT: Nonlinear Parameter-efficient Adaptation of Pre-trained Models](https://huggingface.co/papers/2410.01870) (2024)\n* [SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching](https://huggingface.co/papers/2410.06364) (2024)\n* [GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs](https://huggingface.co/papers/2408.15300) (2024)\n* [SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values](https://huggingface.co/papers/2409.05926) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13848.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13848", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Show-o: One Single Transformer to Unify Multimodal Understanding and Generation](https://huggingface.co/papers/2408.12528) (2024)\n* [VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation](https://huggingface.co/papers/2409.04429) (2024)\n* [MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model](https://huggingface.co/papers/2408.12321) (2024)\n* [MIO: A Foundation Model on Multimodal Tokens](https://huggingface.co/papers/2409.17692) (2024)\n* [Emu3: Next-Token Prediction is All You Need](https://huggingface.co/papers/2409.18869) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13852.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13852", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CoGen: Learning from Feedback with Coupled Comprehension and Generation](https://huggingface.co/papers/2408.15992) (2024)\n* [Aligning Language Models Using Follow-up Likelihood as Reward Signal](https://huggingface.co/papers/2409.13948) (2024)\n* [LLMs Are In-Context Reinforcement Learners](https://huggingface.co/papers/2410.05362) (2024)\n* [WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback](https://huggingface.co/papers/2408.15549) (2024)\n* [RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning](https://huggingface.co/papers/2410.02089) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13854.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13854", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://huggingface.co/papers/2409.02813) (2024)\n* [MMR: Evaluating Reading Ability of Large Multimodal Models](https://huggingface.co/papers/2408.14594) (2024)\n* [MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models](https://huggingface.co/papers/2410.10139) (2024)\n* [MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models](https://huggingface.co/papers/2410.08182) (2024)\n* [FTII-Bench: A Comprehensive Multimodal Benchmark for Flow Text with Image Insertion](https://huggingface.co/papers/2410.12564) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13859.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13859", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training](https://huggingface.co/papers/2410.08202) (2024)\n* [Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models](https://huggingface.co/papers/2409.10197) (2024)\n* [LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation](https://huggingface.co/papers/2408.15881) (2024)\n* [SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference](https://huggingface.co/papers/2410.04417) (2024)\n* [EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model](https://huggingface.co/papers/2408.11795) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2410.13863.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2410.13863", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [MonoFormer: One Transformer for Both Diffusion and Autoregression](https://huggingface.co/papers/2409.16280) (2024)\n* [HART: Efficient Visual Generation with Hybrid Autoregressive Transformer](https://huggingface.co/papers/2410.10812) (2024)\n* [DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation](https://huggingface.co/papers/2410.08159) (2024)\n* [A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation](https://huggingface.co/papers/2410.01912) (2024)\n* [Denoising with a Joint-Embedding Predictive Architecture](https://huggingface.co/papers/2410.03755) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}