Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management
提出一种新的大语言模型微调方法，通过上下文增强、自动构建偏好数据、课程学习和支持约束等技术，使模型生成的评论回复更符合人类偏好，在酒店评论测试中优于现有方法。
Online reviews can shape where people stay, eat, and shop, but businesses often struggle to keep up with the flood of customer feedback. Although generative artificial intelligence (AI) offers a promising solution, general-purpose models are not designed for the specific judgment, tone, and accuracy required in customer review responses. This study introduces a new fine-tuning method that helps large language models generate review replies that better match human preferences in real business settings. The paper makes several technical advances. It identifies why review-response systems hallucinate and introduces a context-augmentation strategy to reduce factual errors. It also develops a theory-driven way to automatically construct preference data from existing review-response records, overcoming a major barrier in preference fine-tuning. In addition, the study proposes a curriculum learning design and a new support-constraint method that reduces the overconservatism of existing offline optimization approaches, with stronger theoretical guarantees. Tests on hotel reviews show that the method produces better responses than leading alternatives in both automated evaluations and human judgments. The findings point to a practical path for using AI to help firms respond faster and more consistently to customers while also underscoring the need for safeguards, human oversight, and domain-specific model alignment in customer-facing AI systems.