Summary of Wilke: Wise-layer Knowledge Editor For Lifelong Knowledge Editing, by Chenhui Hu et al.
WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing
by Chenhui Hu, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach for rectifying inaccuracies in large language models (LLMs) without requiring costly retraining. The current methods focus primarily on single editing, which is insufficient for lifelong learning. The study reveals the limitations of existing approaches, including toxicity buildup and flash, caused by pattern mismatch. A new knowledge editing method, called Wise-Layer Knowledge Editor (WilKE), is introduced to address these challenges. WilKE selects editing layers based on pattern matching degrees across different layers in language models. Experimental results show that WilKE improves editing performance by 46.2% and 67.8% for GPT2-XL and GPT-J, respectively, compared to state-of-the-art methods. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting smarter every day! But sometimes they get stuck with outdated or wrong information. This paper talks about a new way to fix those mistakes without having to retrain the whole model. Right now, most methods only focus on one problem at a time, but that’s not enough for learning over a long time. The researchers found out why this happens and came up with a new approach called Wise-Layer Knowledge Editor (WilKE). It looks at how well different parts of the model match patterns to decide where to make changes. This method works really well, improving editing by 46% and 68% for two popular models. |
Keywords
» Artificial intelligence » Gpt » Pattern matching