Multi-perspective Preference Alignment of LLMs for Programming-Community Question Answering

Abstract

Programming-Community Question Answering (PCQA) aims to tackle issues through generating functional code and guiding descriptions. It involves multiple candidates, with different users having varying preferences for them. Additionally, one may contain outdated APIs. These undoubtedly present a challenge for responsing that meet user preferences. Recently, Reinforcement Learning from Human Feedback demonstrates its ability to precisely control the behavior of large language models (LLMs) to yield human-like responses. However, applying it to LLMs in domain-specific PCQA remains unexplored. In this work, we propose Multi-perspective Preference Alignment for Programming-Community Question Answering to generate user-centric responses, called MupPCQA. It includes three stages: Preference Standardization to control content quality, Preference Integration to consider diverse user tendencies, Preference Timeliness Mitigation to alleviate outdated answers. Extensive experiments on a high-quality, real-world PCQA dataset validate its accuracy and preference. Compared to its base model, MupPCQA shows an improvement of nearly 11% in BLEU, with increases of 20% and 17.5% in BERTScore and CodeBERTScore.