Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization

• URL: http://arxiv.org/abs/2410.02433v1
• Date: Thu, 3 Oct 2024 12:28:13 GMT
• Title: Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization
• Authors: Mingyang Wang, Lukas Lange, Heike Adel, Jannik Strötgen, Hinrich Schütze,
• Abstract summary: Large language models need to be updated regularly. Model editing is challenging as it might also affect knowledge that is unrelated to the new data. We propose SAUL, a streamlined model editing method that uses sentence concatenation with augmented random facts for generation regularization.
• Score: 48.07144492109635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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