Papers › To Err Is Human, but Llamas Can Learn It Too

To Err Is Human, but Llamas Can Learn It Too

8 Mar 2024arXiv:2403.05493archive 2025-07-28

Agnes Luhtaru, Taido Purason, Martin Vainikko, Maksym Del, Mark Fishel

This study explores enhancing grammatical error correction (GEC) through artificial error generation (AEG) using language models (LMs). Specifically, we fine-tune Llama 2-based LMs for error generation and find that this approach yields synthetic errors akin to human errors. Next, we train GEC Llama models with the help of these artificial errors and outperform previous state-of-the-art error correction models, with gains ranging between 0.8 and 6 F0.5 points across all tested languages (German, Ukrainian, and Estonian). Moreover, we demonstrate that generating errors by fine-tuning smaller sequence-to-sequence models and prompting large commercial LMs (GPT-3.5 and GPT-4) also results in synthetic errors beneficially affecting error generation models.

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