Summary of A Theory For Token-level Harmonization in Retrieval-augmented Generation, by Shicheng Xu et al.
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation
by Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors propose a theory to explain and trade off the benefits and drawbacks of retrieval-augmented generation (RAG) in large language models (LLMs). RAG combines retrieved texts with LLMs to provide valuable external information, but also risks introducing noisy or incorrect data that can mislead the model. The paper formalizes the trade-off between the value of this external knowledge and its potential risk, and proposes a novel method called Tok-RAG to achieve collaborative generation between the pure LLM and RAG at the token level. Experiments using popular LLMs such as OPT, LLaMA-2, and Mistral demonstrate the effectiveness of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG in large language models (LLMs) helps by adding new information, but it can also bring bad data that messes up the model. The researchers looked at how to balance this benefit against the risk. They came up with a way to predict what will happen when RAG is used, without needing extra training or information. This helps us make better decisions about using RAG in the future. |
Keywords
» Artificial intelligence » Llama » Rag » Retrieval augmented generation » Token
