GAUGE: When Not to Trust LLM-as-a-Judge in User-Simulated Evaluation of Task-Oriented Agents
Uncovering the reliability gaps in automated evaluation frameworks for task-oriented AI agents.
Comparing and selecting task-oriented LLM agents increasingly relies on a low-cost offline evaluation gate: persona-driven LLM user-simulators converse with each candidate, an LLM-as-a-judge scores the transcripts, and the higher-scoring agent is promoted. We introduce GAUGE, a r...
ArXiv CS.CL
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Automated evaluation using LLM-as-a-judge is often flawed due to inherent biases and a lack of reliable correlation with actual human performance. GAUGE provides a critical framework for identifying when these simulated benchmarks fail, ensuring developers do not deploy underperforming agents based on misleading scores.
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