Summary of Tug-of-war Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-augmented Language Models, by Zhuoran Jin et al.
Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models
by Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Qiuxia Li, Jun Zhao
First submitted to arxiv on: 22 Feb 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 explores the phenomenon of “knowledge conflicts” in Retrieval-Augmented Language Models (RALMs), which have shown great potential in refining and expanding their internal memory by retrieving evidence from external sources. The authors identify two key types of knowledge conflicts: those between an RALM’s internal memory and external sources, and those between truthful, irrelevant, or misleading evidence. Through analysis and experimentation, the researchers reveal that RALMs tend to favor their internal memory over correct external evidence, exhibit availability bias towards common knowledge, and display confirmation bias towards evidence consistent with their internal memory. To address these conflicts, the authors propose a method called Conflict-Disentangle Contrastive Decoding (CD2), which shows promise in calibrating an RALM’s confidence and resolving knowledge conflicts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models that get help from other sources of information can sometimes disagree with themselves. These models, called Retrieval-Augmented Language Models (RALMs), are very good at getting better by looking at what others know. But sometimes they get stuck because they’re not sure what to believe. The researchers found out that RALMs tend to trust their own ideas more than the truth from outside sources, and also like to go with what’s most common. They want to make it easier for RALMs to decide what’s true and what’s not. |
