Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas. However, most existing approaches rely on pre-defined personal profiles, which are not only time-consuming and labor-intensive to create but also lack flexibility. We propose In-Dialogue Learning (IDL), a fine-tuning framework that enhances the ability of pre-trained large language models to leverage dialogue history to characterize persona for completing personalized dialogue generation tasks without pre-defined profiles. Our experiments on three datasets demonstrate that IDL brings substantial improvements, with BLEU and ROUGE scores increasing by up to 200% and 247%, respectively. Additionally, the results of human evaluations further validate the efficacy of our proposed method.

翻译：个性化对话系统因能根据不同的用户画像生成响应而备受关注。然而，现有方法大多依赖预定义的个人画像，这不仅需要耗费大量时间和人力进行构建，且缺乏灵活性。本文提出对话内学习（IDL）——一种微调框架，旨在增强预训练大语言模型利用对话历史刻画用户画像的能力，从而在无预定义画像的情况下完成个性化对话生成任务。在三个数据集上的实验表明，IDL带来了显著性能提升：BLEU和ROUGE评分分别最高提升200%和247%。此外，人工评估结果进一步验证了所提方法的有效性。