Summary of “in Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles Through In-dialogue Learning, by Chuanqi Cheng et al.

Main: Computation and Language (cs.CL)
Secondary: Artificial Intelligence (cs.AI)

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

by Chuanqi Cheng, Quan Tu, Shuo Shang, Cunli Mao, Zhengtao Yu, Wei Wu, Rui Yan

First submitted to arxiv on: 5 Mar 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed In-Dialogue Learning (IDL) framework is a fine-tuning approach that enables pre-trained large language models to learn from dialogue history and generate personalized responses without requiring predefined profiles. IDL demonstrates substantial improvements in BLEU and ROUGE scores, increasing by up to 200% and 247%, respectively, on three datasets. Human evaluations also validate the efficacy of the proposed method.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Personalized dialogue systems can now be used to create more natural conversations by learning from the dialogue history without needing pre-defined profiles. A new fine-tuning framework called In-Dialogue Learning (IDL) makes this possible. IDL is a technique that helps large language models understand what kind of personality each person has based on their conversation. This allows for better responses and more personalized conversations.