Fine-tuned GPT-4o reaches state-of-the-art on grammatical error correction while reference-based metrics underestimate performance by missing 73.76 percent of valid or superior outputs.
Transactions of the Association for Computational Linguistics12, 837–855 (2024)
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Edit-level majority voting on multiple LLM-generated candidates reduces over-correction in grammatical error correction and outperforms greedy and MBR decoding on nine multilingual benchmarks while remaining stable to prompt variations.
citing papers explorer
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Multi-Dimensional Evaluation of LLMs for Grammatical Error Correction
Fine-tuned GPT-4o reaches state-of-the-art on grammatical error correction while reference-based metrics underestimate performance by missing 73.76 percent of valid or superior outputs.
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Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction
Edit-level majority voting on multiple LLM-generated candidates reduces over-correction in grammatical error correction and outperforms greedy and MBR decoding on nine multilingual benchmarks while remaining stable to prompt variations.