arXiv AI July 24, 2026 By Minh Ngoc Ta, My Anh Tran Nguyen, Duong D. Nguyen, Yuxia Wang, Preslav Nakov

One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies

arXiv:2607. 21143v1 Announce Type: cross Abstract: Ambiguous user requests make clarification a sequential decision problem for conversational LLM assistants: they must decide whether to ask, what to ask, when to stop, and when to answer.

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arXiv AI

Jun 3

Uncertainty-Aware Clarification in LLM Agents with Information Gain

arXiv:2606. 03135v1 Announce Type: new Abstract: Large Language Model (LLM) agents often operate under underspecified user instructions, where latent uncertainty over user intent leads to erroneous tool actions.

By Mengyi Deng, Zhiwei Li, Xin Li, Tingyu Zhu, Ying Zhao, Zhijiang Guo, Wei Wang

llms agents

arXiv AI

Aug 18

Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation

arXiv:2608. 15949v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendation accuracy and natural dialogue.

By Cedar Site Bai, Duanshun Li, Zhenyu Liao, Sheikh Sarwar, Huiyuan Chen, Yuan Chen, Changhe Yuan, Haiyang Zhang, Qilin Qi

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Sep 15

Learning Steerable Clarification Policies with Collaborative Self-play

arXiv:2512.04068v3 Announce Type: replace Abstract: To handle underspecified or ambiguous queries, AI assistants need a policy for managing their uncertainty to determine (a) when to guess the user i...

By Jonathan Berant, Maximillian Chen, Adam Fisch, Reza Aghajani, Fantine Huot, Mirella Lapata, Jacob Eisenstein

agents reinforcement-learning

arXiv AI

Aug 11

Reasoning about Intent for Ambiguous Requests

arXiv:2511. 10453v4 Announce Type: replace-cross Abstract: Large language models often respond to ambiguous requests by implicitly committing to one interpretation, frustrating users and creating safety risks when that interpretation is wrong.

By Irina Saparina, Mirella Lapata

llms nlp reinforcement-learning safety

arXiv AI

Sep 23

When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gain

arXiv:2609.24290v1 Announce Type: new Abstract: Instruction-tuned LLMs faced with underspecified queries often commit to a single interpretation rather than ask for clarification, producing confident...

By Yunxiang Li, Xixin Wu, Helen Meng

llms agents reinforcement-learning benchmarks

arXiv Computation and Language

Sep 3

A Tri-Agent Framework for Evaluating and Aligning Question Clarification Capabilities of Large Language Models

The paper presents a tri‑agent framework for evaluating large language models’ question‑clarification abilities. It involves a Question Clarifying Agent that identifies ambiguities and asks follow‑up questions, a Respondent Agent that simulates human replies, and an Evaluator Agent that judges the dialogue using metrics such as ambiguity handling, question quality, dialogue efficiency, language appropriateness, and intent alignment. The authors illustrate the approach with synthetic supply‑chain data and discuss validating the evaluator against human judgments.

By Yikai Zhao, Saurabh Pandey, Pradeep Kumar Misra

llms agents benchmarks safety

One email a morning, machine-written

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Uncertainty-Aware Clarification in LLM Agents with Information Gain

Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation

Learning Steerable Clarification Policies with Collaborative Self-play

Reasoning about Intent for Ambiguous Requests

When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gain

A Tri-Agent Framework for Evaluating and Aligning Question Clarification Capabilities of Large Language Models