PERSA combines RLHF with selective parameter-efficient updates to top transformer layers, raising style alignment scores from 35% to 96% on code feedback benchmarks while holding correctness near 100%.
arXiv preprint arXiv:2404.11973 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Custom tag methods with NMT and LLMs for word alignment in text style transfer perform no better than standard attention-based alignment from NMT models.
citing papers explorer
-
PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs
PERSA combines RLHF with selective parameter-efficient updates to top transformer layers, raising style alignment scores from 35% to 96% on code feedback benchmarks while holding correctness near 100%.
-
Text Style Transfer with Machine Translation for Graphic Designs
Custom tag methods with NMT and LLMs for word alignment in text style transfer perform no better than standard attention-based alignment from NMT models.