Summary of Alphaedit: Null-space Constrained Knowledge Editing For Language Models, by Junfeng Fang et al.
AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
by Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Shi Jie, Xiang Wang, Xiangnan He, Tat-seng Chua
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to model editing, dubbed AlphaEdit, has been proposed to mitigate the disruption of original knowledge within large language models (LLMs) during sequential editing scenarios. The locating-then-editing paradigm, which first identifies influential parameters and then updates them by introducing a perturbation, is effective but prone to disrupting preserved knowledge. AlphaEdit addresses this issue by projecting perturbations onto the null space of the preserved knowledge before applying them to the parameters, ensuring that post-edited LLMs remain unchanged when queried about the preserved knowledge. Experimental results on various LLMs, including LLaMA3, GPT2-XL, and GPT-J, demonstrate a significant boost in performance (36.4% average) with minimal additional code required. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AlphaEdit is a new way to edit large language models so they don’t forget important things. When we update these models, sometimes the old information gets mixed up or lost. AlphaEdit helps fix this by changing how we do the updates. It makes sure that the model still knows what it knew before, even after we make changes. This is a big deal because it means we can improve the model’s performance without worrying about losing important details. |
Keywords
» Artificial intelligence » Gpt
