4 papers
Gradients Must Earn Their Influence: Unifying SFT with Generalized Entropic Objectives
Zecheng Wang, Deyuan Liu, Chunshan Li +5
Standard negative log-likelihood (NLL) for Supervised Fine-Tuning (SFT) applies uniform token-level weighting. This rigidity creates a two-fold failure mode: (i) overemphasizing lo…
LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models
Zhanyue Qin, Yue Ding, Deyuan Liu +7
Nowadays, Large Language Models (LLMs) have attracted widespread attention due to their powerful performance. However, due to the unavoidable exposure to socially biased data durin…
Surrogate Signals from Format and Length: Reinforcement Learning for Solving Mathematical Problems without Ground Truth Answers
Rihui Xin, Han Liu, Zecheng Wang +4
Large Language Models (LLMs) have achieved remarkable success in natural language processing tasks, with Reinforcement Learning (RL) playing a key role in adapting them to specific…
TMGBench: A Systematic Game Benchmark for Evaluating Strategic Reasoning Abilities of LLMs
Haochuan Wang, Xiachong Feng, Lei Li +4
The rapid advancement of large language models has accelerated their application in reasoning, with strategic reasoning drawing increasing attention. To evaluate the strategic reas…