Summary of Astute Rag: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts For Large Language Models, by Fei Wang et al.
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
by Fei Wang, Xingchen Wan, Ruoxi Sun, Jiefeng Chen, Sercan Ö. Arık
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper explores the limitations of Retrieval-Augmented Generation (RAG) in integrating external knowledge to address the shortcomings of large language models (LLMs). Despite its effectiveness, RAG can be undermined by imperfect retrieval, introducing irrelevant or misleading information. The study finds that imperfect retrieval augmentation is inevitable and potentially harmful, highlighting the importance of identifying knowledge conflicts between LLM-internal and external sources. To overcome this bottleneck, the authors propose Astute RAG, a novel approach that iteratively consolidates internal and external knowledge with source-awareness. Experimental results using Gemini and Claude demonstrate that Astute RAG outperforms previous robustness-enhanced RAG methods, achieving performance comparable to LLMs without RAG in worst-case scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how large language models (LLMs) can be improved by combining them with external knowledge. However, this process, called Retrieval-Augmented Generation (RAG), is not perfect and can introduce wrong or misleading information. The study shows that this problem will happen often and can make the results unreliable. To fix this, the authors suggest a new way to do RAG, called Astute RAG, which helps to combine internal and external knowledge better. They tested it on two big language models, Gemini and Claude, and showed that it works much better than other ways of doing RAG. |
Keywords
» Artificial intelligence » Claude » Gemini » Rag » Retrieval augmented generation
