2024/03/05 by Chuanqi Cheng, Cheng, Chuanqi, Quan Tu +11 · 4 citations
Psychology · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Counseling, Therapy, and Family Dynamics #Educational Tools and Methods #FOS: Computer and information sciences #Innovative Teaching and Learning Methods
paper · pdf · doi:10.48550/arxiv.2403.03102
openalex publication_date 2024/03/05 · openalex created_date 2024/03/07 · openalex updated_date 2026/07/28
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.