LANGALIGN：通过跨语言嵌入对齐增强非英语语言模型
LANGALIGN: Enhancing Non-English Language Models via Cross-Lingual Embedding Alignment
AI总结：
提出LANGALIGN方法，通过在语言模型与任务头间对齐英语和目标语言嵌入向量，显著提升韩、日、中三种非英语语言模型性能，并可反向转换数据。
摘要：
尽管大型语言模型已受到关注，但许多服务开发者仍因实际限制而依赖基于嵌入的模型。在这种情况下，微调数据的质量直接影响性能，而英语数据集常被用作训练非英语模型的种子数据。在本研究中，我们提出LANGALIGN，它在语言模型与任务头之间的接口处，通过将英语嵌入向量与目标语言的嵌入向量对齐，来增强目标语言处理能力。在韩语、日语和中文上的实验表明，LANGALIGN在这三种语言上均显著提升了性能。此外，我们展示了LANGALIGN可以反向应用，将目标语言数据转换为英语模型可以处理的格式。
英文摘要：
While Large Language Models have gained attention, many service developers still rely on embedding-based models due to practical constraints. In such cases, the quality of fine-tuning data directly impacts performance, and English datasets are often used as seed data for training non-English models. In this study, we propose LANGALIGN, which enhances target language processing by aligning English embedding vectors with those of the target language at the interface between the language model and the task header. Experiments on Korean, Japanese, and Chinese demonstrate that LANGALIGN significantly improves performance across all three languages. Additionally, we show that LANGALIGN can be applied in reverse to convert target language data into a format that an English-based model can process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
