Iterative Critique-Refine Framework for Enhancing LLM Personalization
This addresses the issue of tone, topic, or style drift in personalized text generation for users, though it is incremental as it builds on retrieval-augmented approaches.
The paper tackled the problem of personalized text generation by proposing PerFine, a training-free critique-refine framework that iteratively refines drafts using profile-grounded feedback, resulting in GEval gains of +7-13% over existing methods like PGraphRAG across datasets such as Yelp, Goodreads, and Amazon.
Personalized text generation requires models not only to produce coherent text but also to align with a target user's style, tone, and topical focus. Existing retrieval-augmented approaches such as LaMP and PGraphRAG enrich profiles with user and neighbor histories, but they stop at generation and often yield outputs that drift in tone, topic, or style. We present PerFine, a unified, training-free critique-refine framework that enhances personalization through iterative, profile-grounded feedback. In each iteration, an LLM generator produces a draft conditioned on the retrieved profile, and a critic LLM - also conditioned on the same profile - provides structured feedback on tone, vocabulary, sentence structure, and topicality. The generator then revises, while a novel knockout strategy retains the stronger draft across iterations. We further study additional inference-time strategies such as Best-of-N and Topic Extraction to balance quality and efficiency. Across Yelp, Goodreads, and Amazon datasets, PerFine consistently improves personalization over PGraphRAG, with GEval gains of +7-13%, steady improvements over 3-5 refinement iterations, and scalability with increasing critic size. These results highlight that post-hoc, profile-aware feedback offers a powerful paradigm for personalized LLM generation that is both training-free and model-agnostic.