Technology Category
Reasoning under Uncertainty: Other Foundations of Reasoning under UncertaintyKnowledge Representation and Reasoning: Diagnosis and Abductive ReasoningMachine Learning: Calibration & Uncertainty Quantification
InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning
🤖 AI Summary
This work addresses the challenge of overconfident and erroneous responses from large language models in high-stakes domains such as healthcare and law, often triggered by ambiguous user queries. To mitigate this, the authors propose a novel active information acquisition framework that integrates domain-specific document retrieval with strategic follow-up questioning. For the first time, Dempster–Shafer evidence theory is incorporated into this process, enabling interpretable fusion of incomplete or conflicting multi-source information through a structured evidence network. This approach explicitly models uncertainty and prevents premature decision-making. Experimental results demonstrate that the method outperforms strong baselines on legal and medical tasks, significantly enhancing decision reliability and system efficiency while reducing the number of required interaction rounds.
Technology Category
Application Category
📝 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 uncertainty using Dempster-Shafer belief assignments over a structured evidential network, enabling principled fusion of incomplete and potentially contradictory evidence from both sources without prematurely collapsing to a definitive answer. Across legal and medical tasks, InfoGatherer outperforms strong baselines while requiring fewer turns. By grounding uncertainty in formal evidential theory rather than heuristic LLM signals, InfoGatherer moves towards trustworthy, interpretable decision support in domains where reliability is critical.
Problem
Research questions and friction points this paper is trying to address.
information seeking
evidence retrieval
strategic questioning
uncertainty modeling
document-grounded QA
Innovation
Methods, ideas, or system contributions that make the work stand out.
evidence retrieval
strategic questioning
Dempster-Shafer theory
uncertainty modeling
interpretable AI
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