Liang Zhu


2024

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DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment
Liang Zhu | Feiteng Fang | Yuelin Bai | Longze Chen | Zhexiang Zhang | Minghuan Tan | Min Yang
Findings of the Association for Computational Linguistics: EMNLP 2024

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CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment
Feiteng Fang | Liang Zhu | Xi Feng | Jinchang Hou | Qixuan Zhao | Chengming Li | Xiping Hu | Ruifeng Xu | Min Yang
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users. However, a longstanding challenge in human alignment techniques based on reinforcement learning lies in their inherent complexity and difficulty in training. To address this challenge, we present a simple yet effective Contrastive Learning Framework for Human Alignment (CLHA) to align LLMs with human preferences directly. CLHA employs a novel rescoring strategy to evaluate the noise within the data by considering its inherent quality and dynamically adjusting the training process. Simultaneously, CLHA utilizes pairwise contrastive loss and adaptive supervised fine-tuning loss to adaptively modify the likelihood of generating responses, ensuring enhanced alignment with human preferences. Using advanced methods, CLHA surpasses other algorithms, showcasing superior performance in terms of reward model scores, automatic evaluations, and human assessments on the widely used “Helpful and Harmless” dataset.