English(EN) Precision--Recall Trade-off on the Inference Level for LLMs in Grammatical Error Correction

LLMs的推理时参数控制语法纠错权衡

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员探讨了在大型语言模型（LLMs）用于语法纠错（GEC）时如何控制精度-召回率权衡。他们发现，在推理过程中调整重复惩罚参数可以在不改变模型架构或训练的情况下实现这种控制。该方法在标准的GEC基准测试中被证明是有效的，并且在概念上类似于先前的Align-and-Predict Decoding等技术，为现有的LLMs提供了一种简单且广泛适用的方法。 AI

影响提供了一种在不重新训练的情况下微调LLM在语法纠错方面输出的方法。

排序理由学术论文，详细介绍了控制LLM在特定任务上行为的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Signal score: 1 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了控制LLM在特定任务上行为的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CL TIER_1 English(EN) · Ryszard Staruch, Filip Grali\'nski · 2026-11-02 05:00

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

arXiv:2610.41698v1 Announce Type: new Abstract: Large language models (LLMs) have recently been adopted for Grammatical Error Correction (GEC), achieving strong performance via both full fine-tuning and prompt-based approaches. As probabilistic sequence models, LLMs expose infere…

本文如何被排名

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

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