arXiv (All)
open access
Benchmarking LLM Judges for Voice-Agent Evaluation: Reliability, Calibration, and Human Oversight
Evaluating conversational voice agents at scale re- quires reliable assessment methods that capture both observ- able interaction quality and the contextual judgment typically provided by human evaluators. We investigate LLM-as-a-Judge evaluation by comparing human judgments with GPT-4.1 and GPT-5 o
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
- On the Reliability of User-Centric Evaluation of Conversational Recommender Systems#1143689
- Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation#1034553
- Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation#978414
- Auditing Bias and Safety in Voice AI Customer Care#927509
- VoiceTrace: A Benchmark and Retrieval Framework for Who-Said-What Speech Retrieval#1012702
Record · ID 1013207
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