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cs.CL2026
R^3: Advertisement Compliance Rectification via Group-Relative Experience Extractor and Curriculum Reinforcement
Yuan Chen, Zhenyu Hu, Mengge Xue +5
Rigorous content moderation is crucial for online advertising but leads to millions of daily rejections. This scale renders manual rectification infeasible, particularly for video…
cs.CL2024
Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
Mengge Xue, Zhenyu Hu, Liqun Liu +5
Multiple-Choice Questions (MCQs) constitute a critical area of research in the study of Large Language Models (LLMs). Previous works have investigated the selection bias problem in…
cs.CL2024
Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding
Kuo Liao, Shuang Li, Meng Zhao +5
Recent strides in large language models (LLMs) have yielded remarkable performance, leveraging reinforcement learning from human feedback (RLHF) to significantly enhance generation…