LLM Roleplay: Simulating Human-Chatbot Interaction

TL;DR

This paper introduces LLM Roleplay, a novel method using large language models to automatically generate diverse, goal-oriented human-chatbot dialogues based on personas, reducing resource needs for user studies.

Contribution

It presents a new persona-based, goal-oriented dialogue generation technique that simulates human-chatbot interactions using LLMs, validated through a user study.

Findings

Generated dialogues are highly indistinguishable from real human-chatbot conversations.

LLMs effectively maintain persona consistency during multi-turn dialogues.

The method enables diverse dialogue generation across sociodemographic groups.

Abstract

The development of chatbots requires collecting a large number of human-chatbot dialogues to reflect the breadth of users' sociodemographic backgrounds and conversational goals. However, the resource requirements to conduct the respective user studies can be prohibitively high and often only allow for a narrow analysis of specific dialogue goals and participant demographics. In this paper, we propose LLM Roleplay: a goal-oriented, persona-based method to automatically generate diverse multi-turn dialogues simulating human-chatbot interaction. LLM Roleplay can be applied to generate dialogues with any type of chatbot and uses large language models (LLMs) to play the role of textually described personas. To validate our method, we collect natural human-chatbot dialogues from different sociodemographic groups and conduct a user study to compare these with our generated dialogues. We evaluate the capabilities of state-of-the-art LLMs in maintaining a conversation during their embodiment of a specific persona and find that our method can simulate human-chatbot dialogues with a high indistinguishability rate.