InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning
Maksym Taranukhin, Shuyue Stella Li, Evangelos Milios, Geoff Pleiss, Yulia Tsvetkov, Vered Shwartz

TL;DR
InfoGatherer is a framework that improves information gathering for high-stakes question-answering by systematically combining evidence from documents and user interactions, leading to more reliable and interpretable decisions.
Contribution
It introduces a principled uncertainty modeling approach using Dempster-Shafer theory to fuse evidence from multiple sources in LLM-based QA systems.
Findings
Outperforms baselines on legal and medical tasks
Requires fewer interaction turns for accurate answers
Provides more trustworthy and interpretable outputs
Abstract
LLMs are increasingly deployed in high-stakes domains such as medical triage and legal assistance, often as document-grounded QA systems in which a user provides a description, relevant sources are retrieved, and an LLM generates a prediction. In practice, initial user queries are often underspecified, and a single retrieval pass is insufficient for reliable decision-making, leading to incorrect and overly confident answers. While follow-up questioning can elicit missing information, existing methods typically depend on implicit, unstructured confidence signals from the LLM, making it difficult to determine what remains unknown, what information matters most, and when to stop asking questions. We propose InfoGatherer, a framework that gathers missing information from two complementary sources: retrieved domain documents and targeted follow-up questions to the user. InfoGatherer models…
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Taxonomy
TopicsTopic Modeling · Explainable Artificial Intelligence (XAI) · Logic, Reasoning, and Knowledge
