ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference

• URL: http://arxiv.org/abs/2312.02554v2
• Date: Mon, 26 Feb 2024 08:51:03 GMT
• Title: ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference
• Authors: Tianchi Cai, Xierui Song, Jiyan Jiang, Fei Teng, Jinjie Gu, Guannan Zhang
• Abstract summary: A typical alignment procedure consists of supervised fine-tuning and preference learning. We introduce Point-wise Direct Preference Optimization, a novel preference learning method designed to harness point-wise feedback effectively. Our work also uncovers a novel connection between supervised fine-tuning and point-wise preference learning, culminating in Unified Language Model Alignment.
• Score: 16.73260713938154
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Aligning language models to human expectations, e.g., being helpful and harmless, has become a pressing challenge for large language models. A typical alignment procedure consists of supervised fine-tuning and preference learning. Most preference learning methods, such as RLHF and DPO, depend on pairwise preference data, which inadequately address scenarios where human feedback is point-wise, leading to potential information loss and suboptimal performance. Addressing this gap, we introduce Point-wise Direct Preference Optimization, a novel preference learning method designed to harness point-wise feedback effectively. Our work also uncovers a novel connection between supervised fine-tuning and point-wise preference learning, culminating in Unified Language Model Alignment, a single-step method that unifies the alignment with human demonstrations and point-wise preferences. Extensive experiments on point-wise preference datasets with binary or continuous labels validate the effectiveness of our methods. Our code and a new dataset with high-quality demonstration samples on harmlessness are released.

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