Summary of Is Bigger Edit Batch Size Always Better? — An Empirical Study on Model Editing with Llama-3, by Junsang Yoon et al.
Is Bigger Edit Batch Size Always Better? – An Empirical Study on Model Editing with Llama-3
by Junsang Yoon, Akshat Gupta, Gopala Anumanchipalli
First submitted to arxiv on: 1 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 study investigates the effectiveness of popular model editing techniques (ROME, MEMIT, and EMMET) on Llama-3, a large language model. The analysis targets precise layer interventions using up to 4096 edits across three strategies: sequential editing, batch editing, and hybrid sequential-batch editing. Findings show that increasing edit batch-sizes can significantly degrade model performance, whereas sequential editing may be more effective for equal numbers of edits. This study highlights the importance of considering both batched and sequential editing methods in scaling model editing techniques, which could lead to future research optimizing batch sizes and model editing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how to make changes to a big language model called Llama-3. The team tested different ways to edit the model’s layers, using up to 4,096 edits. They found that making too many changes at once can actually hurt the model’s performance. Instead, making smaller changes one by one might be better. This study shows that we need to think about how we make changes to these big models in order to improve them. |
Keywords
» Artificial intelligence » Language model » Large language model » Llama
