“在对话中我们学习”：通过对话中学习实现无需预定义用户画像的个性化对话
"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
AI总结：
提出In-Dialogue Learning (IDL)微调框架，使预训练大语言模型能利用对话历史刻画人物画像，在无需预定义画像的情况下完成个性化对话生成，并在三个数据集上取得显著提升。
摘要：
个性化对话系统近年来因能生成符合不同人物设定的回复而备受关注。然而，多数现有方法依赖预定义的个人画像，这不仅创建耗时费力，还缺乏灵活性。我们提出In-Dialogue Learning (IDL)，这是一个微调框架，旨在增强预训练大语言模型利用对话历史来刻画人物画像的能力，从而在无需预定义画像的情况下完成个性化对话生成任务。我们在三个数据集上的实验表明，IDL带来了显著提升，BLEU和ROUGE分数分别最高提升了200%和247%。此外，人类评估结果进一步验证了所提方法的有效性。
英文摘要：
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.
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。
