4 citations · 5 across the 6 of their papers we have counts for
7 papers
PLOT: Enhancing Preference Learning via Optimal Transport
Liang Zhu, Yuelin Bai, Xiankun Ren +6
Preference learning in Large Language Models (LLMs) has advanced significantly, yet existing methods remain limited by modest performance gains, high computational costs, hyperpara…
DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment
Liang Zhu, Feiteng Fang, Yuelin Bai +4
Reinforcement Learning from Human Feedback (RLHF), using algorithms like Proximal Policy Optimization (PPO), aligns Large Language Models (LLMs) with human values but is costly and…
From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion
Liang Zhu, Haolin Chen, Lidong Zhao +1
While Large Language Models (LLMs) have demonstrated exceptional proficiency in code completion, they typically adhere to a Hard Completion (HC) paradigm, compelling the generation…
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity
Feiteng Fang, Dingwei Chen, Xiang Huang +10
Currently, most reinforcement learning tasks focus on domains like mathematics and programming, where verification is relatively straightforward. However, in subjective tasks such…
PersonaMath: Boosting Mathematical Reasoning via Persona-Driven Data Augmentation
Jing Luo, Longze Chen, Run Luo +12
While closed-source Large Language Models (LLMs) demonstrate strong mathematical problem-solving abilities, open-source models still face challenges with such tasks. To bridge this…
DeliLaw: A Chinese Legal Counselling System Based on a Large Language Model
Nan Xie, Yuelin Bai, Hengyuan Gao +7
Traditional legal retrieval systems designed to retrieve legal documents, statutes, precedents, and other legal information are unable to give satisfactory answers due to lack of s…