Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916422056148992 |
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| author | Wang, Mingyang Lange, Lukas Adel, Heike Strötgen, Jannik Schütze, Hinrich |
| author_facet | Wang, Mingyang Lange, Lukas Adel, Heike Strötgen, Jannik Schütze, Hinrich |
| contents | To ensure large language models contain up-to-date knowledge, they need to be updated regularly. However, model editing is challenging as it might also affect knowledge that is unrelated to the new data. State-of-the-art methods identify parameters associated with specific knowledge and then modify them via direct weight updates. However, these locate-and-edit methods suffer from heavy computational overhead and lack theoretical validation. In contrast, directly fine-tuning the model on requested edits affects the model's behavior on unrelated knowledge, and significantly damages the model's generation fluency and consistency. To address these challenges, we propose SAUL, a streamlined model editing method that uses sentence concatenation with augmented random facts for generation regularization. Evaluations on three model editing benchmarks show that SAUL is a practical and reliable solution for model editing outperforming state-of-the-art methods while maintaining generation quality and reducing computational overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02433 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization Wang, Mingyang Lange, Lukas Adel, Heike Strötgen, Jannik Schütze, Hinrich Computation and Language Machine Learning To ensure large language models contain up-to-date knowledge, they need to be updated regularly. However, model editing is challenging as it might also affect knowledge that is unrelated to the new data. State-of-the-art methods identify parameters associated with specific knowledge and then modify them via direct weight updates. However, these locate-and-edit methods suffer from heavy computational overhead and lack theoretical validation. In contrast, directly fine-tuning the model on requested edits affects the model's behavior on unrelated knowledge, and significantly damages the model's generation fluency and consistency. To address these challenges, we propose SAUL, a streamlined model editing method that uses sentence concatenation with augmented random facts for generation regularization. Evaluations on three model editing benchmarks show that SAUL is a practical and reliable solution for model editing outperforming state-of-the-art methods while maintaining generation quality and reducing computational overhead. |
| title | Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.02433 |