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Tags: retrieval-augmented generation* + arxiv* + large language models*

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1. RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs

   A method that uses instruction tuning to adapt LLMs for knowledge-intensive tasks. RankRAG simultaneously trains the models for context ranking and answer generation, enhancing their retrieval-augmented generation (RAG) capabilities.

   2024-07-10 Tags: natural language processing, large language models, instruction tuning, context ranking, retrieval-augmented generation, nvidia, arxiv by klotz
2. Paper page - Context Tuning for Retrieval Augmented Generation

   2023-12-13 Tags: llm, rag, context, metadata, search, arxiv by klotz
3. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

   Retrieval-Augmented Generation (RAG) models, which merge pre-trained parametric and non-parametric memory systems, such as Wikipedia, to enhance language generation in knowledge-intensive NLP tasks, outperforming traditional seq2seq models and setting new standards in open domain QA tasks​

   2023-11-16 Tags: llm, rag, arxiv by klotz

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