Composable Interventions for Language Models

- The paper presents a study of the composability of various interventions applied to large language models (LLMs). - Composability is important for practical deployment, as it allows multiple modifications to be made without requiring retraining from scratch. - The authors find that aggressive compression struggles with composing well with other interventions, while editing and unlearning can be quite composable depending on the technique used. - They recommend expanding the scope of interventions studied and investigating scaling laws for composability as future work.

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