Summary of Vera: Validation and Enhancement For Retrieval Augmented Systems, by Nitin Aravind Birur et al.
VERA: Validation and Enhancement for Retrieval Augmented systems
by Nitin Aravind Birur, Tanay Baswa, Divyanshu Kumar, Jatan Loya, Sahil Agarwal, Prashanth Harshangi
First submitted to arxiv on: 18 Sep 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 This paper proposes VERA, a system to evaluate and enhance the performance of Retrieval-Augmented Generation (RAG) models in large language modeling. RAG models rely on embedded knowledge but often produce inaccurate responses. To address this issue, VERA assesses and refines the retrieved context before response generation, ensuring precision and minimizing errors. The evaluator-cum-enhancer LLM checks if external retrieval is necessary, evaluates the relevance of the retrieved context, and refines it to eliminate non-essential information. Post-response generation, VERA splits the response into atomic statements, assesses their relevance to the query, and ensures adherence to the context. Experimental results show that VERA improves the performance of smaller open-source models and larger state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VERA is a system designed to help large language models (LLMs) give more accurate answers by fixing some common problems with how they work. Right now, LLMs are really good at generating text, but sometimes what they come up with isn’t true or relevant to the question being asked. VERA tries to fix this by checking if the information it’s using is actually important and making sure that what it comes up with makes sense in the context of the question. |
Keywords
» Artificial intelligence » Precision » Rag » Retrieval augmented generation
