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arXiv · 2403.03102

"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning

Chuanqi Cheng·Quan Tu·Shuo Shang·Cunli Mao·Zhengtao Yu·Wei Wu·Rui Yan
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Abstract

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.

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Chuanqi Cheng, Quan Tu, Shuo Shang, Cunli Mao, Zhengtao Yu, Wei Wu, Rui Yan. 2024-03-05. "In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning. https://arxiv.org/abs/2403.03102

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