让同一问题对不同用户生成个性化回答，提升对话适配性。
Personalized LLM for Generating Customized Responses to the Same Query from Different Users
- 设计双塔模型，融合通用与用户专属编码器，捕捉个体差异。
- 通过对比学习使同用户对话表示靠近，异用户则分离，提升区分度。
- 构建多用户数据集MQDialog，支持跨语言个性化研究。
现有大模型个性化工作通常为模型分配不同角色，却忽视提问者的多样性。本文提出一种新的用户感知型个性化方法，使相同问题针对不同用户生成差异化回答。设计双塔模型架构，包含跨用户通用编码器和用户专属编码器，并采用多视角增强的对比学习，拉近同一用户的对话表示，推远不同用户间的表示。为缓解查询多样性对对比学习的影响，按查询相似性聚类对话，限制对比学习范围。针对缺乏专门用户感知数据集的问题，构建了来自英汉剧本及微信记录的多用户数据集MQDialog，包含173名提问者和12名回复者。大量实验表明，该方法显著提升个性化响应质量，在ROUGE-L得分上相对基线提升8.4%至48.7%，胜率在54%至82%之间。
原文摘要 · Abstract (English)
Existing work on large language model (LLM) personalization assigned different responding roles to LLMs, but overlooked the diversity of queriers. In this work, we propose a new form of querier-aware LLM personalization, generating different responses even for the same query from different queriers. We design a dual-tower model architecture with a cross-querier general encoder and a querier-specific encoder. We further apply contrastive learning with multi-view augmentation, pulling close the dialogue representations of the same querier, while pulling apart those of different queriers. To mitigate the impact of query diversity on querier-contrastive learning, we cluster the dialogues based on query similarity and restrict the scope of contrastive learning within each cluster. To address the lack of datasets designed for querier-aware personalization, we also build a multi-querier dataset from English and Chinese scripts, as well as WeChat records, called MQDialog, containing 173 queriers and 12 responders. Extensive evaluations demonstrate that our design significantly improves the quality of personalized response generation, achieving relative improvement of 8.4% to 48.7% in ROUGE-L scores and winning rates ranging from 54% to 82% compared with various baseline methods.
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