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Renjie Hua

2 accepted papers

2025

Icon2: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation

EMNLP 2025

Large Language Models (LLMs) require high quality preference datasets to align with human preferences. However, conventional methods for constructing such datasets face significant challenges: reliance on pre-collected instructions often leads to distribution mismatches with target models, while the

2024

CMD: a framework for Context-aware Model self-Detoxification

EMNLP 2024main

Text detoxification aims to minimize the risk of language models producing toxic content. Existing detoxification methods of directly constraining the model output or further training the model on the non-toxic corpus fail to achieve a decent balance between detoxification effectiveness and generati…