No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions

Questions help unlock information to satisfy users' information needs.However, when the question is poorly posed, answerers (whether human or computer) may struggle to answer the question in a way that satisfies the asker, despite possibly knowing everything necessary to address the asker's latent information need.Using Reddit question-answer interactions from r/NoStupidQuestions, we develop a computational framework grounded in linguistic theory to study poorly-posedness of questions by generating spaces of potential interpretations of questions and computing distributions over these spaces based on interpretations chosen by both human answerers in the Reddit question thread, as well as by a suite of large language models.Both humans and models struggle to converge on dominant interpretations when faced with poorly posed questions, but employ different strategies: humans focus on specific interpretations through question negotiation, while models attempt comprehensive coverage by addressing many interpretations simultaneously.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.acl-long.160
Citations
1
Primary Topic
Education and Critical Thinking Development
Type
article
Field-Weighted Citation Impact
8.93

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article

No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions

Neha Srikanth, Rachel Rudinger, Jordan Lee Boyd-Graber
1 citations
Education and Critical Thinking Development
8.93
article

No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions

Neha Srikanth, Rachel Rudinger, Jordan Lee Boyd-Graber
article en
1 citations

Abstract

Questions help unlock information to satisfy users' information needs.However, when the question is poorly posed, answerers (whether human or computer) may struggle to answer the question in a way that satisfies the asker, despite possibly knowing everything necessary to address the asker's latent information need.Using Reddit question-answer interactions from r/NoStupidQuestions, we develop a computational framework grounded in linguistic theory to study poorly-posedness of questions by generating spaces of potential interpretations of questions and computing distributions over these spaces based on interpretations chosen by both human answerers in the Reddit question thread, as well as by a suite of large language models.Both humans and models struggle to converge on dominant interpretations when faced with poorly posed questions, but employ different strategies: humans focus on specific interpretations through question negotiation, while models attempt comprehensive coverage by addressing many interpretations simultaneously.

University of Maryland, Baltimore (US), University of Maryland, College Park (US)
National Science Foundation, National Institutes of Health
Openalex Percentile: Top 3%
Education and Critical Thinking Development
8.93
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