When can transformers reason with abstract symbols?
Paper
•
2310.09753
•
Published
•
2
Research on LLM
-
-
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 28 -
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
Paper • 2310.09520 • Published • 10 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 52 -
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
Paper • 2310.11441 • Published • 26 -
ControlLLM: Augment Language Models with Tools by Searching on Graphs
Paper • 2310.17796 • Published • 16 -
Ultra-Long Sequence Distributed Transformer
Paper • 2311.02382 • Published • 2 -
Can LLMs Follow Simple Rules?
Paper • 2311.04235 • Published • 10 -
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
Paper • 2311.04254 • Published • 13 -
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
Paper • 2311.04257 • Published • 20 -
FlashDecoding++: Faster Large Language Model Inference on GPUs
Paper • 2311.01282 • Published • 35 -
Language Models can be Logical Solvers
Paper • 2311.06158 • Published • 18 -
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 74 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 77 -
Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster
Paper • 2311.08263 • Published • 15 -
Contrastive Chain-of-Thought Prompting
Paper • 2311.09277 • Published • 34 -
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Paper • 2309.03883 • Published • 33 -
FLM-101B: An Open LLM and How to Train It with $100K Budget
Paper • 2309.03852 • Published • 43 -
Effective Long-Context Scaling of Foundation Models
Paper • 2309.16039 • Published • 30 -
Zephyr: Direct Distillation of LM Alignment
Paper • 2310.16944 • Published • 122 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 87 -
The ART of LLM Refinement: Ask, Refine, and Trust
Paper • 2311.07961 • Published • 10 -
Interpreting Pretrained Language Models via Concept Bottlenecks
Paper • 2311.05014 • Published • 1 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 242 -
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Paper • 2311.06754 • Published • 1 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 138 -
Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses
Paper • 2312.00763 • Published • 19 -
LLM in a flash: Efficient Large Language Model Inference with Limited Memory
Paper • 2312.11514 • Published • 258 -
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Paper • 2304.13712 • Published -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 64 -
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
Paper • 2401.02954 • Published • 41 -
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
Paper • 2401.12954 • Published • 29 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 79 -
H2O-Danube-1.8B Technical Report
Paper • 2401.16818 • Published • 17 -
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models
Paper • 2402.10524 • Published • 22 -
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 64