Summary of Instructedit: Instruction-based Knowledge Editing For Large Language Models, by Ningyu Zhang et al.
InstructEdit: Instruction-based Knowledge Editing for Large Language Models
by Ningyu Zhang, Bozhong Tian, Siyuan Cheng, Xiaozhuan Liang, Yi Hu, Kouying Xue, Yanjie Gou, Xi Chen, Huajun Chen
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); 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 abstract discusses the limitations of current knowledge editing techniques for large language models (LLMs) and proposes a novel instruction-based approach to address these issues. Specifically, it presents InstructEdit, a method that enables a single editor to adapt to various task performances simultaneously using simple instructions. Experimental results demonstrate an average 14.86% increase in Reliability in multi-task editing settings, surpassing previous strong baselines. The study also analyzes the underlying mechanisms of instruction-based knowledge editing, revealing that instructions can help control optimization direction with stronger out-of-distribution (OOD) generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be edited to change their behavior without hurting performance. But current methods are limited because they need a different editor for each task, making it hard to apply them more broadly. The authors propose an instruction-based approach called InstructEdit that lets one editor work on many tasks at once using simple instructions. This makes editing easier and faster. They tested this method and found it works better than previous methods. |
Keywords
* Artificial intelligence * Generalization * Multi task * Optimization
