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6 days ago
6 days ago
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Collections including paper arxiv:2312.10997
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Oct 28
Oct 28
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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 • 10 -
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs
Paper • 2406.10251 • Published
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Apr 19
Apr 19
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Apr 17
Apr 17
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 11 -
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 • 2
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Feb 22
Feb 22
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Jan 9
Jan 9
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Jan 9
Jan 9
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Jun 27
Jun 27
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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 • 79 -
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
Collection by
Oct 16
Oct 16
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Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 74 -
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 • 10
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about 1 hour ago
about 1 hour ago
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Context Tuning for Retrieval Augmented Generation
Paper • 2312.05708 • Published • 17 -
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 • 36 -
Retrieval-Augmented Generation for Large Language Models: A Survey
Paper • 2312.10997 • Published • 10