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：个性化对话系统因其能够生成符合不同人格特征的回应而近年来受到广泛关注。然而，现有方法大多依赖预定义的个性化档案，这不仅耗时耗力，且缺乏灵活性。我们提出对话内学习（In-Dialogue Learning, IDL）这一微调框架，能够增强预训练大语言模型利用对话历史刻画人格特征的能力，从而无需预定义档案即可完成个性化对话生成任务。在三个数据集上的实验表明，IDL带来了显著提升，其中BLEU和ROUGE分数分别最高提升200%和247%。此外，人工评估结果进一步验证了我们提出方法的有效性。