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.

个人化对话系统一直受到广泛关注，因其能够根据不同人物角色生成响应。本文提出了In-Dialogue Learning (IDL)框架，通过细调预训练的大型语言模型，利用对话历史来描述角色，从而完成个性化对话生成任务，而无需预先定义角色。我们在三个数据集上的实验证明，IDL显著提升了模型性能，BLEU和ROUGE分数分别增加了200%和247%。此外，人工评估的结果进一步验证了我们提出方法的有效性。

对话中我们学习：通过对话学习实现无预定义个人化对话