Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search (original) (raw)

Joko, Hideaki, Chatterjee, Shubham, Ramsay, Andrew ORCID: https://orcid.org/0000-0002-1451-8973, de Vries, Arjen P., Dalton, Jeff and Hasibi, Faegheh(2024) Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search. In: SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington D.C., USA, 14-18 Jul 2024, pp. 796-806. ISBN 9798400704314(doi: 10.1145/3626772.3657815)

Abstract

The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. When compared to existing datasets, LAPS-produced conversations are as natural and diverse as expert-created ones, which stays in contrast with fully synthetic methods. The collected dataset is suited to train preference extraction and personalized response generation. Our results show that responses generated explicitly using extracted preferences better match user's actual preferences, highlighting the value of using extracted preferences over simple dialogue history. Overall, LAPS introduces a new method to leverage LLMs to create realistic personalized conversational data more efficiently and effectively than previous methods.

University Staff: Request a correction | Enlighten Editors: Update this record

Funder and Project Information

Dalton-UKRI-Turing Fellow

Jeff Dalton

EP/V025708/1

Computing Science

Deposit and Record Details