Addressing Implicit Assumptions in Maternal Health Question Answering

Core Concepts

Addressing the implicit assumptions and implications embedded in user questions is crucial for providing complete and helpful answers in sensitive domains like maternal and infant health.

Abstract

The authors study how users embed pragmatic inferences, such as presuppositions and implicatures, in questions about maternal and infant health, and how these inferences are naturally addressed by human experts when answering these questions.
Key highlights:

The authors collected a dataset of 2,727 pragmatic inferences from 500 questions across three diverse sources: a maternal health QA system, Reddit, and the Natural Questions dataset.
They found that users are more likely to make false or subjective inferences through implicatures rather than presuppositions.
When an inference is false, it is more likely to be naturally addressed in the expert-written answer.
The authors experiment with augmenting QA pipelines to address pragmatic inferences, showing that this can lead to more complete and helpful answers, especially for questions with highly plausible false assumptions.
Automatically generating pragmatic inferences is challenging, as language models struggle to capture the nuanced domain knowledge required, particularly for implicatures.
The authors conclude that next-generation QA systems must learn to address the pragmatics of user questions to provide helpful and trustworthy information in sensitive domains.

Stats

"Newborns can safely drink non-dairy milk."
"All infections and illness can pass through breast milk."
"Avoiding an epidural contributes to a more "natural" and unmedicated birthing experience."
"Using different bottles or nipples for feeding may compromise the baby's latch."
"Fetuses have the ability to form memories."

Quotes

"Questions posed by information-seeking users often contain implicit false or potentially harmful assumptions."
"Complete answers to these types of questions must not only address the surface question itself, but also 'question the question', critically examining its pragmatic needs."
"Language models have been shown to exhibit sycophancy, sometimes adjusting responses to align with a human user's view. However, helpful QA systems should not only challenge false or subjective assumptions in questions by verifying them against a vetted corpus, but also infer the asker's intent to make sure that its answer satisfactorily addresses their deeper information needs."

Key Insights Distilled From

Pregnant Questions

by Neha Srikant... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2311.09542.pdf

Deeper Inquiries

How can QA systems be trained to proactively identify and address pragmatic inferences, particularly implicatures, in user questions?

To train QA systems to proactively identify and address pragmatic inferences, especially implicatures, in user questions, several key steps can be taken:

Dataset Collection: Gather a diverse set of questions from various sources, including domain-specific QA systems, forums, and open-domain question-answering datasets. These questions should cover a wide range of topics and user backgrounds to capture different types of pragmatic inferences.

Annotation Scheme: Develop an annotation scheme that prompts annotators to identify assumptions and implications in user questions. Annotators should be trained to recognize both presuppositions and implicatures and differentiate between them.

Expert Annotations: Engage domain experts to annotate the dataset with pragmatic inferences. Experts can provide valuable insights into the types of inferences users make and how they should be addressed in answers.

Model Training: Train QA models using the annotated dataset, incorporating mechanisms to detect and address pragmatic inferences. This may involve fine-tuning language models with prompts that guide them to consider implicatures and presuppositions in user questions.

Evaluation: Evaluate the performance of the trained models by comparing their answers to expert-written responses. Metrics like ROUGE, BLEURT, and QAFACTEVAL can be used to assess the quality and completeness of the generated answers.

Iterative Improvement: Continuously refine the training data, annotation scheme, and model architecture based on feedback from experts and evaluation results. This iterative process can help enhance the system's ability to handle pragmatic inferences effectively.

By following these steps, QA systems can be equipped to proactively identify and address pragmatic inferences, including implicatures, in user questions, leading to more informative and contextually relevant answers.

How can the insights from this work on pragmatic inference be applied to improve the trustworthiness and helpfulness of conversational AI systems more broadly?

The insights gained from the study on pragmatic inference can be leveraged to enhance the trustworthiness and helpfulness of conversational AI systems in various domains beyond maternal and infant health:

Contextual Understanding: AI systems can be trained to recognize and respond to implicit assumptions and implications in user queries, improving the contextual understanding of the conversation. This can lead to more accurate and relevant responses.

Error Correction: By detecting false or misleading inferences in user questions, conversational AI systems can proactively correct misinformation and prevent the propagation of harmful beliefs. This can enhance the credibility and reliability of the system.

Personalization: Understanding the pragmatic needs of users allows AI systems to tailor responses to individual preferences and information requirements. By addressing implicit beliefs and concerns, the system can provide more personalized and engaging interactions.

Ethical Considerations: Incorporating pragmatic inference analysis can help AI systems navigate sensitive topics and ethical dilemmas more effectively. By considering the implicit assumptions and implications in user queries, the system can uphold ethical standards and promote responsible AI use.

Continuous Learning: AI systems can be designed to adapt and learn from user interactions, refining their understanding of pragmatic inferences over time. This continuous learning process can improve the system's ability to engage with users in a meaningful and supportive manner.

By applying the insights from pragmatic inference research, conversational AI systems can become more empathetic, trustworthy, and valuable tools for users across various domains, fostering better communication and information exchange.