Summary of Model Editing at Scale Leads to Gradual and Catastrophic Forgetting, by Akshat Gupta et al.
Model Editing at Scale leads to Gradual and Catastrophic Forgetting
by Akshat Gupta, Anurag Rao, Gopala Anumanchipalli
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 explores the challenges of editing large language models to correct incorrect facts and update them with new information. Current techniques have shown promise, but they are typically evaluated using metrics that focus on reliability, specificity, and generalization over a single edit. The authors argue that for model editing to be practical, it must be able to handle multiple edits to the same model. To evaluate existing methods at scale, the researchers focused on two state-of-the-art techniques: ROME and MEMIT. They found that as models are edited sequentially with multiple facts, they gradually but progressively forget previously learned information, followed by an abrupt or catastrophic forgetting phase. Both types of forgetting limit the usefulness of model editing methods at scale, making them less effective for downstream tasks. The study highlights other limitations of ROME and MEMIT at scale, pushing for the development and evaluation of more scalable model editing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Editing large language models is important because it allows us to correct mistakes and add new information. Right now, there are ways to edit models, but they’re not very good at handling many changes. The authors looked at two popular methods, ROME and MEMIT, to see how well they work when editing a model many times. They found that the more you edit a model, the more it forgets what it learned before. This makes it hard to use edited models for important tasks like answering questions or generating text. The study shows that we need better ways to edit models so they can handle many changes without forgetting. |
Keywords
» Artificial intelligence » Generalization
