PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs

This paper studies RLHF for shaping an LLM’s feedback into a professor-like style while preserving diagnostic accuracy. It’s relevant for teams building personalized tutoring or critique systems, especially where tone control and correctness must be balanced.

cs.AI updates on arXiv.org · May 6 · 1 min read · score 9.0

From the source

arXiv:2605.01123v1 Announce Type: new Abstract: Large language models (LLMs) can provide automated feedback in educational settings, but aligning an LLMs style with a specific instructors tone while maintaining diagnostic correctness remains challenging. We ask how can we update an LLM for automated feedback generation to align with a target instructors style without sacrificing core knowledge? We study how Reinforcement Learning from Human Feedback (RLHF) can adapt a transformer-based LLM to…