Hadi Khalaf, Sajani Vithana, Kunal Talreja, Haewon Jeong, Viveck R. Cadambe, Flavio Calmon · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30076 · open-access PDF
Language models make it easy to build classifiers by simply prompting a model with the sample to classify. However, their predictions are neither deterministic nor uniformly reliable across samples. How can we combine predictions from different models to improve reliability? We view LLM classifiers as noisy measurement instruments of an underlying target label. Our goal is to maximize average classification accuracy at a given average query cost. To this end, we introduce circuits that organize LLM classifiers into an adaptive sequence of queries and decisions. We then characterize the optimal
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