VQV Signal
This study compares human evaluations with GPT-4.1 and GPT-5 in assessing telecom and retail voice-agent conversations, focusing on conversational quality and safety. It explores the reliability and calibration of large language models as judges for voice-agent performance.
This study compares human evaluations with GPT-4.1 and GPT-5 in assessing telecom and retail voice-agent conversations, focusing on conversational quality and safety. It explores the reliability and calibration of large language models as judges for voice-agent performance.
AI-assisted summary based on the listed source.
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 on telecom and...
Reliable and scalable evaluation methods are crucial for improving conversational voice agents, especially to capture nuanced human judgments. Using LLMs like GPT-4.1 and GPT-5 could enhance assessment consistency and reduce the need for extensive human oversight.
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GPT-4.1 has a source-backed funding with coverage spanning research.
VQV surfaced this signal because it is recent, relevant to AI Voice, connected to arXiv.
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