arXiv Computation and Language November 2, 2026 By Ryszard Staruch, Filip Grali\'nski

Precision--Recall Trade-off on the Inference Level for LLMs in Grammatical Error Correction

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The paper examines how large language models (LLMs) can be tuned for grammatical error correction (GEC) by adjusting inference-time parameters. It demonstrates that the repetition penalty can be used to control the precision–recall trade-off during decoding, without changing the model architecture, training, or prompt. Experiments on standard GEC benchmarks confirm that this simple, broadly applicable method allows inference-time adjustment of the precision–recall balance, similar to prior sequence‑to‑sequence techniques such as Align‑and‑Predict Decoding.

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