A Tri-Agent Framework for Evaluating and Aligning Question Clarification Capabilities of Large Language Models
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Large Language Models (LLMs) are increasingly deployed in interactive systems where understanding user intent precisely is paramount. A key capability for such systems is effective question clarification, especially when user queries are ambiguous or underspecified. This paper introduces a novel tri-agent framework for the robust evaluation of an LLM's ability to engage in clarifying dialogue. Our framework comprises three distinct LLM-based agents: (1) a Question Clarifying Agent (QCA), the system under evaluation, tasked with identifying ambiguities and posing clarifying questions; (2) a Respondent Agent (RA), designed to simulate human user responses, potentially including irrelevant or challenging replies; and (3) an Evaluator Agent (EA), an LLM-as-a-judge, which assesses the quality of the dialogue based on a comprehensive set of metrics. We detail a methodology for synthetic data generation in the supply chain domain as an example. We propose metrics evaluating ambiguity handling, question quality, dialogue efficiency, language appropriateness, and final intent alignment. We also briefly discuss the validation of the EA against human judgments. This work provides a structured approach to benchmark, validate, and improve the clarification capabilities of conversational LLM applications.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 11 of its 32 references that are in the map. First posted on arXiv 2 Sep 2026.
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11 of 32 references.
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- 2025InfoQuest: Evaluating Multi-Turn Dialogue Agents for Open-Ended Conversations with Hidden Context17 Feb
- 2024Aligning Language Models to Explicitly Handle Ambiguity18 Apr
Other references · 9
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
- 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
- 2024Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferencescited by 144
- 2019Asking Clarifying Questions in Open-Domain Information-Seeking Conversationscited by 139
- 2019Survey on Evaluation Methods for Dialogue Systemscited by 126
- 2024ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialogcited by 24
- 2024AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMscited by 7
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- 2025Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate
- 2025ClarifyMT-Bench: Benchmarking and Improving Multi-Turn Clarification for Conversational Large Language Models
- 2024Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types
- 2024DialSim: A Dialogue Simulator for Evaluating Long-Term Multi-Party Dialogue Understanding of Conversational Agents