Summary of Ripplecot: Amplifying Ripple Effect Of Knowledge Editing in Language Models Via Chain-of-thought In-context Learning, by Zihao Zhao et al.
RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning
by Zihao Zhao, Yuchen Yang, Yijiang Li, Yinzhi Cao
First submitted to arxiv on: 4 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 The paper addresses a significant challenge in knowledge editing for large language models (LLMs) known as the “ripple effect”. When editing a single fact, LLMs struggle to accurately update related facts in a sequence. Recent strategies have moved away from traditional parameter updates to more flexible methods, but these still face limitations. In-context learning (ICL) editing uses demonstrations like “Imagine that + new fact” to guide LLMs, but struggles with complex multi-hop questions. Memory-based editing maintains additional storage for edits and related facts, requiring continuous updates. To address this challenge, the paper proposes RippleCOT, a novel ICL editing approach integrating Chain-of-Thought (COT) reasoning. RippleCOT structures demonstrations as “newfact, question, thought, answer”, incorporating a thought component to identify and decompose multi-hop logic within questions. This approach effectively guides LLMs through complex multi-hop questions with chains of related facts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about solving a problem in AI called the “ripple effect”. It’s hard for big language models to update lots of related information when we change one piece of information. Some methods are better than others, but they still have some limitations. The researchers propose a new way to do this that uses Chain-of-Thought reasoning and does a much better job. |
