Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search

TL;DR

This paper introduces LAPS, a novel method using large language models to efficiently generate large-scale, realistic, multi-session personalized dialogues, overcoming data scarcity for personalized conversational agents.

Contribution

LAPS leverages LLMs to guide human workers in creating diverse, multi-domain personalized dialogues, improving scalability and quality over expert-only data collection methods.

Findings

LAPS-produced dialogues are as natural and diverse as expert-created ones.

Responses using extracted preferences better match user preferences.

LAPS accelerates data collection for personalized conversational AI.

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.