To Err Is Human, But Llamas Can Learn It Too

Luhtaru Agnes, Purason Taido, Vainikko Martin, Del Maksym, Fishel Mark. Arxiv 2024

[Paper]    
Fine Tuning GPT Model Architecture Pretraining Methods Prompting Training Techniques

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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