  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.
