InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning

2026-03-06

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

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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

• information seeking
• evidence retrieval
• strategic questioning
• uncertainty modeling
• document-grounded QA

Innovation

• evidence retrieval
• strategic questioning
• Dempster-Shafer theory
• uncertainty modeling
• interpretable AI