Summary of Unraveling and Mitigating Retriever Inconsistencies in Retrieval-augmented Large Language Models, by Mingda Li et al.
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
by Mingda Li, Xinyu Li, Yifan Chen, Wenfeng Xuan, Weinan Zhang
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 investigates the limitations of Retrieval-Augmented Large Language Models (RALMs) in achieving consistent performance. While RALMs excel in factuality, they do not consistently outperform original retrieval-free Language Models (LMs). The authors identify four categories that contribute to this inconsistency: degeneration behavior, knowledge sources, and reader model unpredictability. To address this issue, the paper proposes Ensemble of Retrievers (EoR), a trainable framework that can adaptively retrieve information from different sources and reduce unpredictable errors. Experimental results on Open Domain Question Answering show that EoR improves performance over RALMs by decreasing inconsistent behaviors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well language models do when they use additional information to answer questions. Even though these models are good, they don’t always get the answers right. The study finds that there are four reasons why this happens: how the model gets its knowledge, what kind of knowledge it has, and how well the model can understand what’s asked of it. To fix this problem, the researchers created a new way for language models to use information from different sources and reduce mistakes. They tested this approach on answering questions and found that it works better than using just one source of information. |
Keywords
» Artificial intelligence » Question answering
