"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
<|reference_start|>"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning: 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.<|reference_end|>
@article{cheng2024"in,
    title={"In Dialogues We Learn": Towards Personalized Dialogue Without
  Pre-defined Profiles through In-Dialogue Learning},
    author={Chuanqi Cheng, Quan Tu, Shuo Shang, Cunli Mao, Zhengtao Yu, Wei Wu,
  Rui Yan},
    journal={arXiv preprint arXiv:2403.03102},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03102},
    primaryClass={cs.CL cs.AI}
}