SUMFORU: 一种基于LLM的个性化购买决策支持的评论摘要框架
SUMFORU: An LLM-Based Review Summarization Framework for Personalized Purchase Decision Support
AI总结：
SUMFORU通过可调节的多元对齐方法，实现个性化购买决策支持，结合高质量数据管道和两阶段对齐流程，提升摘要的一致性、基础性和偏好对齐性能。
摘要：
在线产品评论包含丰富但嘈杂的信号，这些信号会压垮用户并阻碍有效决策。现有的基于LLM的摘要工具仍然通用，无法考虑个体偏好，限制了其实际效用。我们提出了SUMFORU，一种可调节的评论摘要框架，通过与显式用户人设对齐来支持个性化购买决策。我们的方法整合了一个高质量的数据管道，该管道基于Amazon 2023评论数据集，并采用两阶段对齐流程：（1）通过非对称知识蒸馏进行人设感知的监督微调（SFT），（2）使用偏好估计器进行强化学习与AI反馈（RLAIF）以捕捉细粒度、人设相关信号。我们评估了该模型在基于规则、基于LLM和人类中心的度量标准下的表现，证明在一致性、基础性和偏好对齐方面有持续改进。我们的框架在所有评估设置中均取得最高性能，并能有效泛化到未见的产品类别。我们的结果凸显了可调节的多元对齐在构建下一代个性化决策支持系统中的潜力。
英文摘要：
Online product reviews contain rich but noisy signals that overwhelm users and hinder effective decision-making. Existing LLM-based summarizers remain generic and fail to account for individual preferences, limiting their practical utility. We propose SUMFORU, a steerable review summarization framework that aligns outputs with explicit user personas to support personalized purchase decisions. Our approach integrates a high-quality data pipeline built from the Amazon 2023 Review Dataset with a two-stage alignment procedure: (1) persona-aware Supervised Fine-Tuning (SFT) via asymmetric knowledge distillation, and (2) Reinforcement Learning with AI Feedback (RLAIF) using a preference estimator to capture fine-grained, persona-relevant signals. We evaluate the model across rule-based, LLM-based, and human-centered metrics, demonstrating consistent improvements in consistency, grounding, and preference alignment. Our framework achieves the highest performance across all evaluation settings and generalizes effectively to unseen product categories. Our results highlight the promise of steerable pluralistic alignment for building next-generation personalized decision-support systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
