New LLM Fine-Tuning Method Aligns AI with Human Preferences for Online Review Management
Researchers propose a novel fine-tuning method for large language models (LLMs) to align generative AI with domain-specific human preferences in online review management. Online reviews significantly influence consumer decisions, and managerial responses impact customer relationships and firm performance. However, many reviews go unanswered due to labor constraints. While generative AI excels at general tasks, it often fails to align with human preferences in specialized domains like review responses. Fine-tuning addresses this but faces challenges: hallucinations, difficulty representing domain-specific preferences, and over-conservatism in offline policy optimization. The new method aims to overcome these issues, improving AI-generated responses to online reviews. The paper was published on arXiv (ID: 2604.21209) as a new announcement.
Key facts
- Online reviews are crucial for consumer decision-making.
- Managerial review responses impact customer relationship management and firm performance.
- Many online reviews remain unaddressed due to labor limitations.
- Generative AI models are general-purpose and may not align with domain-specific human preferences.
- Fine-tuning is used to adapt general AI models to specific domains.
- Challenges in fine-tuning include hallucinations, representing domain-specific preferences, and over-conservatism in offline policy optimization.
- A novel preference fine-tuning method is proposed to address these challenges.
- The paper is available on arXiv with ID 2604.21209.
Entities
Institutions
- arXiv