arXiv (All)
open access
A Tri-Agent Framework for Evaluating and Aligning Question Clarification Capabilities of Large Language Models
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- PADMÉ: Preference Alignment Data Synthesis for Meta-Evaluation of LM Agent Evaluators#1130928
- Cross-linguistic semantics for questions#701697
- The Natural Language Interaction Protocol and Standard for AI Agents#927003
- ATLAS: Dual-Horizon Diagnostic Evaluation for Industrial Tool-Use Agents#818740
- ATLAS: Dual-Horizon Diagnostic Evaluation for Industrial Tool-Use Agents#808748
Record · ID 822461
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive