mlsanityData to 14 Oct 2026

Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering

arXiv 2311.09542 ↗First posted 16 Nov 2023cs.CLcited by 14

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Abstract (arXiv)

Questions posed by information-seeking users often contain implicit false or potentially harmful assumptions. In a high-risk domain such as maternal and infant health, a question-answering system must recognize these pragmatic constraints and go beyond simply answering user questions, examining them in context to respond helpfully. To achieve this, we study assumptions and implications, or pragmatic inferences, made when mothers ask questions about pregnancy and infant care by collecting a dataset of 2,727 inferences from 500 questions across three diverse sources. We study how health experts naturally address these inferences when writing answers, and illustrate that informing existing QA pipelines with pragmatic inferences produces responses that are more complete, mitigating the propagation of harmful beliefs.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 20 of its 39 references that are in the map. First posted on arXiv 16 Nov 2023.

Builds on

20 of 39 references.

References · 20

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8 more
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Citing papers per year: 2025: 5; 2026: 9.

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  1. 2025Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challengescited by 31
  2. 2025Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositionscited by 15
  3. 2025LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are Highcited by 12
  4. 2025NLI under the Microscope: What Atomic Hypothesis Decomposition Revealscited by 11
  5. 2025Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questionscited by 10
  6. 2026The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errorscited by 1
  7. 2026ObGynLongBench: Revealing the Evidence-to-EHR Gap in Longitudinal EHR Decision-Makingcited by 1
  8. 2026MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communicationcited by 0
  9. 2026What Patients Really Ask: Exploring the Effect of False Assumptions in Patient Information Seekingcited by 0
  10. 2026When Contextual Inference Fails: Cancelability in Interactive Instruction Followingcited by 0
  11. 2026DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answeringcited by 0
  12. 2026DRInQ: Evaluating Conversational Implicature with Controlled Context Variationcited by 0
2 more
  1. 2026Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversationscited by 0
  2. 2026Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectivelycited by 0