Summary of What External Knowledge Is Preferred by Llms? Characterizing and Exploring Chain Of Evidence in Imperfect Context, By Zhiyuan Chang et al.
What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context
by Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Qing Wang, Yihao Huang, Yang Liu
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach is proposed to improve the performance of large language models (LLMs) when handling multi-hop question-answering tasks. The researchers draw inspiration from criminal procedural law’s Chain of Evidence (CoE), which emphasizes maintaining relevance and mutual support among evidence pieces. This framework is applied to LLMs, which prefer knowledge that is relevant to the question and mutually supports other pieces of information. An automated CoE discrimination approach is proposed, and its effectiveness is evaluated on five different LLMs. The results show that incorporating CoE leads to more accurate generation, stronger answer faithfulness, better robustness against knowledge conflict, and improved performance in a popular Retrieval-Augmented Generation (RAG) case. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve the quality of large language models’ answers by introducing an external knowledge framework. The idea is to provide LLMs with relevant and supportive information that helps them answer complex questions more accurately. The approach is inspired by legal procedures, where evidence must be relevant and mutually supporting to build a strong case. The study evaluates the effectiveness of this approach on five different large language models and finds that it improves their performance in answering questions. |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation
