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Related Experiment Videos

A disambiguation framework for refining and answering ambiguous questions.

Nahyeong Kim1, Ho-Young Jung2

  • 1Department of Artificial Intelligence, Kyungpook National University, Daegu, 41566, Republic of Korea.

Scientific Reports
|July 3, 2026
PubMed
Summary

This study introduces SemRec-SV, a new framework for resolving ambiguous questions. It improves question answering by focusing on recovering and verifying semantic information, leading to more reliable answers.

Keywords:
Answer predictionClarifying question generationLarge language modelsQuestion disambiguation

Related Experiment Videos

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Ambiguous questions pose challenges for reliable answer generation.
  • Current systems often assume complete information recovery after clarification.
  • A gap exists in explicitly modeling the semantic state during ambiguity resolution.

Purpose of the Study:

  • To propose SemRec-SV, a semantic-state-centric framework for ambiguity resolution.
  • To explicitly model semantic-state recovery, verification, and refinement for question answering.
  • To enhance the reliability and performance of clarification-based question answering systems.

Main Methods:

  • Developed SemRec-SV framework with FTGate for ambiguity detection.
  • Implemented semantic-state recovery from clarification interactions.
  • Incorporated StateVerify for assessing state sufficiency and adaptive refinement.

Main Results:

  • SemRec-SV achieved the highest overall performance on the ClarifyingQA benchmark.
  • Outperformed clarification-only baselines and the CLAM baseline across multiple language models.
  • Semantic-state recovery, verification, and adaptive refinement demonstrated significant benefits.

Conclusions:

  • Explicitly modeling semantic information recovery, verification, and refinement enhances ambiguity resolution.
  • SemRec-SV offers a more robust approach than direct answer generation from clarification.
  • The framework improves answer reliability by identifying and addressing incomplete information recovery.