Summary of Do You Know What You Are Talking About? Characterizing Query-knowledge Relevance For Reliable Retrieval Augmented Generation, by Zhuohang Li et al.
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation
by Zhuohang Li, Jiaxin Zhang, Chao Yan, Kamalika Das, Sricharan Kumar, Murat Kantarcioglu, Bradley A. Malin
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The paper proposes a statistical framework to assess the relevance of user queries to retrieval augmented generation (RAG) systems. This approach aims to improve the quality of generated responses by detecting out-of-knowledge queries with low relevance and identifying significant shifts in query distributions that indicate outdated knowledge corpora. The proposed online testing procedure uses goodness-of-fit tests to inspect query relevance, while the offline framework examines a collection of user queries to detect distribution shifts. Experimental results on eight question-answering datasets demonstrate the effectiveness of this approach in enhancing the reliability of existing RAG systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve some problems with language models by making them better at answering questions. Right now, they can get confused and give wrong answers. The researchers came up with a new way to test if language models are good at answering specific questions. They also created a system that checks if the knowledge base used by the language model is still relevant or not. This helps make sure the answers given by the language model are accurate and reliable. |
Keywords
» Artificial intelligence » Knowledge base » Language model » Question answering » Rag » Retrieval augmented generation
