3 papers
cs.AI2024
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision
Zihan Wang, Yunxuan Li, Yuexin Wu +4
Process supervision, using a trained verifier to evaluate the intermediate steps generated by a reasoner, has demonstrated significant improvements in multi-step problem solving. I…
cs.CL2024
Enabling Language Models to Implicitly Learn Self-Improvement
Ziqi Wang, Le Hou, Tianjian Lu +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that ther…
cs.CL2024
Distilling Text Style Transfer With Self-Explanation From LLMs
Chiyu Zhang, Honglong Cai, Yuezhang +4
Text Style Transfer (TST) seeks to alter the style of text while retaining its core content. Given the constraints of limited parallel datasets for TST, we propose CoTeX, a framewo…