Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward
This work addresses adapting and personalizing LLMs for applications like writing assistants and coding agents, but it is incremental as it builds on existing feedback types and domains.
The paper tackles the problem of fine-tuning large language models (LLMs) using user-edit deployment data, which unifies preferences, supervision, and reward feedback, and shows that a proposed ensembling procedure outperforms methods using individual feedback types on adapted domains.
We study how to fine-tune LLMs using user-edit deployment data consisting of a set of context, an agent's response, and user edits. This deployment data is naturally generated by users in applications such as LLMs-based writing assistants and coding agents. The _natural_ origin of user edits makes it a desired source for adapting and personalizing LLMs. In this setup, there emerges a unification of various feedback types namely preferences, supervised labels, and cost that are typically studied separately in the literature. In this paper, we initiate the theoretical investigation of learning from user edits. We first derive bounds for learning algorithms that learn from each of these feedback types. We prove that these algorithms have different trade-offs depending upon the user, data distribution, and model class. We then propose a simple ensembling procedure to jointly learn from these feedback types. On two domains adapted from Gao et al. 2024, we show our ensembling procedure outperforms these methods that learn from individual feedback. Further, we show that our proposed procedure can robustly adapt to different user-edit distributions at test time.