Evaluation-as-Search: Adaptive Discovery of Grounding Failures in Meeting Assistants
LLM-powered meeting assistants are deployed at scale, yet systematic evaluation of their grounding fidelity remains limited to static benchmarks that miss failure modes tied to specific discourse structures or reasoning demands. We propose Evaluation-as-Search (EaS), a feedback-driven methodology that frames quality evaluation as an adaptive search over the space of natural questions a meeting participant might ask. Rather than sampling uniformly, EaS learns from evaluator feedback across iterations to concentrate probing effort on cognitive demands where failures are most likely, guided by a UCB-scored coverage map and blind multi-dimensional quality evaluation. Using EaS, we construct M...
| Authors | Sami Khairy, Yasaman Hosseinkashi, Vishak Gopal et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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