- 
	
	
	
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
Paper • 2403.14403 • Published • 7 - 
	
	
	
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation
Paper • 2406.19215 • Published • 31 - 
	
	
	
Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
Paper • 2410.01782 • Published • 10 - 
	
	
	
LLM-Independent Adaptive RAG: Let the Question Speak for Itself
Paper • 2505.04253 • Published • 13 
Collections
Discover the best community collections!
Collections including paper arxiv:2406.19215 
						
					
				- 
	
	
	
Unlocking Continual Learning Abilities in Language Models
Paper • 2406.17245 • Published • 30 - 
	
	
	
Can Few-shot Work in Long-Context? Recycling the Context to Generate Demonstrations
Paper • 2406.13632 • Published • 5 - 
	
	
	
Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding
Paper • 2406.19263 • Published • 10 - 
	
	
	
Can LLMs Learn by Teaching? A Preliminary Study
Paper • 2406.14629 • Published • 21 
- 
	
	
	
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 59 - 
	
	
	
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 48 - 
	
	
	
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
Paper • 2406.04594 • Published • 8 - 
	
	
	
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Paper • 2406.04271 • Published • 30 
- 
	
	
	
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
Paper • 2312.16862 • Published • 31 - 
	
	
	
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
Paper • 2312.17172 • Published • 30 - 
	
	
	
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers
Paper • 2401.01974 • Published • 7 - 
	
	
	
From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
Paper • 2401.01885 • Published • 28 
- 
	
	
	
PDFTriage: Question Answering over Long, Structured Documents
Paper • 2309.08872 • Published • 53 - 
	
	
	
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 81 - 
	
	
	
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper • 2310.09263 • Published • 41 - 
	
	
	
Context-Aware Meta-Learning
Paper • 2310.10971 • Published • 17 
- 
	
	
	
Octo-planner: On-device Language Model for Planner-Action Agents
Paper • 2406.18082 • Published • 48 - 
	
	
	
Adaptable Logical Control for Large Language Models
Paper • 2406.13892 • Published • 1 - 
	
	
	
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation
Paper • 2406.19215 • Published • 31 - 
	
	
	
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
Paper • 2405.14831 • Published • 5 
- 
	
	
	
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31 - 
	
	
	
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
Paper • 2406.12824 • Published • 21 - 
	
	
	
Tokenization Falling Short: The Curse of Tokenization
Paper • 2406.11687 • Published • 16 - 
	
	
	
Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level
Paper • 2406.11817 • Published • 13 
- 
	
	
	
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Paper • 2402.15627 • Published • 38 - 
	
	
	
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 53 - 
	
	
	
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks
Paper • 2403.00522 • Published • 46 - 
	
	
	
Stealing Part of a Production Language Model
Paper • 2403.06634 • Published • 91 
- 
	
	
	
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
Paper • 2403.14403 • Published • 7 - 
	
	
	
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation
Paper • 2406.19215 • Published • 31 - 
	
	
	
Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
Paper • 2410.01782 • Published • 10 - 
	
	
	
LLM-Independent Adaptive RAG: Let the Question Speak for Itself
Paper • 2505.04253 • Published • 13 
- 
	
	
	
PDFTriage: Question Answering over Long, Structured Documents
Paper • 2309.08872 • Published • 53 - 
	
	
	
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 81 - 
	
	
	
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper • 2310.09263 • Published • 41 - 
	
	
	
Context-Aware Meta-Learning
Paper • 2310.10971 • Published • 17 
- 
	
	
	
Octo-planner: On-device Language Model for Planner-Action Agents
Paper • 2406.18082 • Published • 48 - 
	
	
	
Adaptable Logical Control for Large Language Models
Paper • 2406.13892 • Published • 1 - 
	
	
	
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation
Paper • 2406.19215 • Published • 31 - 
	
	
	
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
Paper • 2405.14831 • Published • 5 
- 
	
	
	
Unlocking Continual Learning Abilities in Language Models
Paper • 2406.17245 • Published • 30 - 
	
	
	
Can Few-shot Work in Long-Context? Recycling the Context to Generate Demonstrations
Paper • 2406.13632 • Published • 5 - 
	
	
	
Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding
Paper • 2406.19263 • Published • 10 - 
	
	
	
Can LLMs Learn by Teaching? A Preliminary Study
Paper • 2406.14629 • Published • 21 
- 
	
	
	
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31 - 
	
	
	
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
Paper • 2406.12824 • Published • 21 - 
	
	
	
Tokenization Falling Short: The Curse of Tokenization
Paper • 2406.11687 • Published • 16 - 
	
	
	
Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level
Paper • 2406.11817 • Published • 13 
- 
	
	
	
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 59 - 
	
	
	
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 48 - 
	
	
	
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
Paper • 2406.04594 • Published • 8 - 
	
	
	
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Paper • 2406.04271 • Published • 30 
- 
	
	
	
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Paper • 2402.15627 • Published • 38 - 
	
	
	
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 53 - 
	
	
	
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks
Paper • 2403.00522 • Published • 46 - 
	
	
	
Stealing Part of a Production Language Model
Paper • 2403.06634 • Published • 91 
- 
	
	
	
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
Paper • 2312.16862 • Published • 31 - 
	
	
	
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
Paper • 2312.17172 • Published • 30 - 
	
	
	
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers
Paper • 2401.01974 • Published • 7 - 
	
	
	
From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
Paper • 2401.01885 • Published • 28