Summary of Cutting Off the Head Ends the Conflict: a Mechanism For Interpreting and Mitigating Knowledge Conflicts in Language Models, by Zhuoran Jin et al.
Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models
by Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
First submitted to arxiv on: 28 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 The paper presents a novel approach to mitigate knowledge conflicts in language models (LMs) by introducing an external context. The authors find that some attention heads have opposite effects in later layers, where memory heads recall internal memory and context heads retrieve external context. They reveal that the pivotal point for knowledge conflicts is the integration of inconsistent information flows. To address this, they propose a method called Pruning Head via PatH PatcHing (PH3), which prunes conflicting attention heads without updating model parameters. PH3 improves LMs’ performance on open-domain QA tasks and demonstrates cross-model, relation, and format generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how language models can learn more by combining their own memory with information from outside sources. Right now, when this happens, it can cause problems because the model’s internal memory and external context don’t always agree. The authors want to understand why this happens and find ways to fix it. They found that some parts of the model have opposite effects, depending on whether they’re looking at internal memory or external context. To solve this problem, they created a new method called PH3, which gets rid of these conflicting parts without changing the rest of the model. This helps the model do better on certain types of questions and shows that it works well across different models, relationships, and formats. |
Keywords
» Artificial intelligence » Attention » Generalization » Pruning » Recall
