Follow-Up Questions Improve Documents Generated by Large Language Models

• URL: http://arxiv.org/abs/2407.12017v2
• Date: Thu, 15 Aug 2024 07:12:33 GMT
• Title: Follow-Up Questions Improve Documents Generated by Large Language Models
• Authors: Bernadette J Tix,
• Abstract summary: This study investigates the impact of Large Language Models (LLMs) generating follow-up questions in response to user requests for short (1-page) text documents. Users interacted with a novel web-based AI system designed to ask follow-up questions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigates the impact of Large Language Models (LLMs) generating follow-up questions in response to user requests for short (1-page) text documents. Users interacted with a novel web-based AI system designed to ask follow-up questions. Users requested documents they would like the AI to produce. The AI then generated follow-up questions to clarify the user's needs or offer additional insights before generating the requested documents. After answering the questions, users were shown a document generated using both the initial request and the questions and answers, and a document generated using only the initial request. Users indicated which document they preferred and gave feedback about their experience with the question-answering process. The findings of this study show clear benefits to question-asking both in document preference and in the qualitative user experience. This study further shows that users found more value in questions which were thought-provoking, open-ended, or offered unique insights into the user's request as opposed to simple information-gathering questions.

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