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Collections including paper arxiv:2312.10997 
						
					
				- 
	
	
	
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 78 - 
	
	
	
REST: Retrieval-Based Speculative Decoding
Paper • 2311.08252 • Published - 
	
	
	
Active Retrieval Augmented Generation
Paper • 2305.06983 • Published • 3 - 
	
	
	
Retrieval-Augmented Generation for Large Language Models: A Survey
Paper • 2312.10997 • Published • 12 
- 
	
	
	
Searching for Best Practices in Retrieval-Augmented Generation
Paper • 2407.01219 • Published • 11 - 
	
	
	
Retrieval-Augmented Generation for Large Language Models: A Survey
Paper • 2312.10997 • Published • 12 - 
	
	
	
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs
Paper • 2406.10251 • Published 
- 
	
	
	
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 13 - 
	
	
	
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Paper • 2401.08406 • Published • 37 - 
	
	
	
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Paper • 2104.08663 • Published • 3 - 
	
	
	
Precise Zero-Shot Dense Retrieval without Relevance Labels
Paper • 2212.10496 • Published • 4 
- 
	
	
	
Dense X Retrieval: What Retrieval Granularity Should We Use?
Paper • 2312.06648 • Published • 1 - 
	
	
	
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 82 - 
	
	
	
Text Embeddings Reveal (Almost) As Much As Text
Paper • 2310.06816 • Published • 1 - 
	
	
	
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Paper • 2401.08406 • Published • 37 
- 
	
	
	
Context Tuning for Retrieval Augmented Generation
Paper • 2312.05708 • Published • 16 - 
	
	
	
Dense X Retrieval: What Retrieval Granularity Should We Use?
Paper • 2312.06648 • Published • 1 - 
	
	
	
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
Paper • 2401.18059 • Published • 47 - 
	
	
	
Retrieval-Augmented Generation for Large Language Models: A Survey
Paper • 2312.10997 • Published • 12 
- 
	
	
	
Searching for Best Practices in Retrieval-Augmented Generation
Paper • 2407.01219 • Published • 11 - 
	
	
	
Retrieval-Augmented Generation for Large Language Models: A Survey
Paper • 2312.10997 • Published • 12 - 
	
	
	
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs
Paper • 2406.10251 • Published 
- 
	
	
	
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 13 - 
	
	
	
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Paper • 2401.08406 • Published • 37 - 
	
	
	
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Paper • 2104.08663 • Published • 3 - 
	
	
	
Precise Zero-Shot Dense Retrieval without Relevance Labels
Paper • 2212.10496 • Published • 4 
- 
	
	
	
Dense X Retrieval: What Retrieval Granularity Should We Use?
Paper • 2312.06648 • Published • 1 - 
	
	
	
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 82 - 
	
	
	
Text Embeddings Reveal (Almost) As Much As Text
Paper • 2310.06816 • Published • 1 - 
	
	
	
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Paper • 2401.08406 • Published • 37 
- 
	
	
	
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 78 - 
	
	
	
REST: Retrieval-Based Speculative Decoding
Paper • 2311.08252 • Published - 
	
	
	
Active Retrieval Augmented Generation
Paper • 2305.06983 • Published • 3 - 
	
	
	
Retrieval-Augmented Generation for Large Language Models: A Survey
Paper • 2312.10997 • Published • 12 
- 
	
	
	
Context Tuning for Retrieval Augmented Generation
Paper • 2312.05708 • Published • 16 - 
	
	
	
Dense X Retrieval: What Retrieval Granularity Should We Use?
Paper • 2312.06648 • Published • 1 - 
	
	
	
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
Paper • 2401.18059 • Published • 47 - 
	
	
	
Retrieval-Augmented Generation for Large Language Models: A Survey
Paper • 2312.10997 • Published • 12