17个模型对比：单模型跨语言纠错，Gemma 9B表现最优。
Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Models
- 用单一模型处理英德意瑞四语语法纠错，统一输入输出机制。
- 6个模型在四语言上均提升正确性，Gemma 9B误差减少最多。
- 适合需要轻量级多语言纠错的场景，如教育工具或写作辅助。
近期语言模型已能胜任多种语言任务，且可理解不同语言输入。本文探究17个主流模型在英语、德语、意大利语和瑞典语中使用单一模型进行语法纠错的表现。分析模型生成结果时，重点关注减少语法错误的同时保持修改幅度小。研究揭示各模型存在的问题，并为多语言语法纠错任务提供推荐方案。结果显示六种模型在四种语言中均提升语法正确性，其中Gemma 9B在所考虑语言中表现最佳。
原文摘要 · Abstract (English)
Recent language models can successfully solve various language-related tasks, and many understand inputs stated in different languages. In this paper, we explore the performance of 17 popular models used to correct grammatical issues in texts stated in English, German, Italian, and Swedish when using a single model to correct texts in all those languages. We analyze the outputs generated by these models, focusing on decreasing the number of grammatical errors while keeping the changes small. The conclusions drawn help us understand what problems occur among those models and which models can be recommended for multilingual grammatical error correction tasks. We list six models that improve grammatical correctness in all four languages and show that Gemma 9B is currently the best performing one for the languages considered.
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