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arXiv · 2610.24250

Can This LLM Annotate My Data? Estimating Zero-Shot LLM Performance

Tong Lin·Grant Schoenebeck·Sabina Tomkins
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Abstract

Large language models (LLMs) are increasingly used in annotation tasks with varying success. While several studies have demonstrated the opportunities and shortcomings of LLMs for annotation, a critical question remains unanswered: is it possible to predict how well they will perform on a new dataset? To answer this and related questions, we construct two task families which induce 461 text classification datasets. We find that it is possible to predict LLM performance - to a certain degree - and that contrary to other work which highlights the usefulness of LLM verbalizations of self-confidence, internal log-probabilities are the most predictive signal.

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Tong Lin, Grant Schoenebeck, Sabina Tomkins. 2026-10-16. Can This LLM Annotate My Data? Estimating Zero-Shot LLM Performance. https://arxiv.org/abs/2610.24250

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