Summary of A Unified Framework For Model Editing, by Akshat Gupta et al.
A Unified Framework for Model Editing
by Akshat Gupta, Dev Sajnani, Gopala Anumanchipalli
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 presents a unified framework for two prominent model editing algorithms, ROME and MEMIT. The main difference between the two is the ability to perform batched edits, with ROME limited to single-edits and MEMIT capable of batched edits. The authors optimize for a preservation-memorization objective, which they call the “preservation-memorization” goal. They demonstrate that ROME can be generalized to enable batched editing using an equality constraint, resulting in EMMET – an Equality-constrained Mass Model Editing algorithm for Transformers. EMMET outperforms MEMIT on multiple dimensions with a batch-size of up to 10,000. The paper shows that both algorithms are equivalent in terms of their optimization objective, abilities, and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research brings together two important editing techniques called ROME and MEMIT. These methods help improve models by making small changes. The main difference between them is that one can only make small changes at a time, while the other can make many changes all at once. The researchers found a way to combine these two approaches into a single method that can do both – called EMMET. This new method is just as good as the original methods and can make big changes too! Both ROME and MEMIT are important for improving models, and this study shows they’re actually very similar. |
Keywords
* Artificial intelligence * Optimization
