Summary of Robust and Scalable Model Editing For Large Language Models, by Yingfa Chen et al.
Robust and Scalable Model Editing for Large Language Models
by Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper focuses on improving the editing capabilities of large language models (LLMs) by allowing them to rely more heavily on contextual knowledge when presented with conflicting information. Current LLMs tend to ignore contextual knowledge and fail to fall back on their parametric knowledge when the context is irrelevant, making it difficult to update or correct their knowledge without retraining. The authors propose a new method, EREN (Edit models by REading Notes), which utilizes proper prompting techniques to make LLMs more controllable by contextual knowledge and robust to irrelevant context. To evaluate the robustness of model editors, the authors collect a new dataset containing challenging irrelevant questions. Empirical results show that EREN outperforms current state-of-the-art methods by a large margin, particularly in integrating knowledge from multiple edits and responding correctly to syntactically similar but semantically unrelated inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart computer program that can understand and respond to human language. Right now, if someone asks it something irrelevant or incorrect, the program tends to ignore what they’re saying and stick with its own knowledge. This makes it hard to teach the program new things without starting from scratch. A team of researchers has come up with a way to make these programs more flexible and able to learn from context. They created a new method called EREN that allows the program to use information from what’s being said around it, rather than just relying on its own knowledge. This makes it easier to update or correct the program without having to retrain it entirely. |
Keywords
» Artificial intelligence » Prompting
