LLMs多语言翻译中的信息损失：训练数据、语言邻近度与语系的作用
Information Loss in LLMs' Multilingual Translation: The Role of Training Data, Language Proximity, and Language Family
AI总结：
本研究系统调查了训练数据量、语言邻近度和语系对LLMs多语言翻译信息损失的影响，发现丰富的训练数据可缓解语言差异，且语言间结构关系是翻译质量的关键预测因子。
摘要：
大型语言模型在多语言翻译方面取得了令人瞩目的进展，但它们在某些语言对上仍面临挑战——特别是那些训练数据有限或与英语存在显著语言差异的语言对。本研究系统性地调查了训练数据、语言邻近度和语系如何影响多语言翻译中的信息损失。我们通过执行往返翻译评估了两个大型语言模型，GPT-4和Llama 2。翻译质量使用BLEU分数和BERT相似度指标进行评估。我们的结果揭示了训练数据量和语言距离之间存在稳健的交互作用：丰富的训练数据可以缓解语言差异带来的影响；在低资源条件下，结构上更接近英语的语言始终产生更高的翻译质量。在各种距离指标中，正字法距离、系统发生距离、句法距离和地理距离作为翻译性能的强预测因子出现。语系也施加了独立的影响。这些发现有助于更深入地理解塑造大型语言模型多语言翻译的语言约束，强调翻译质量不仅受数据量影响，还受语言间的结构和类型学关系影响。
英文摘要：
Large language models have achieved impressive progress in multilingual translation, yet they continue to face challenges with certain language pairs-particularly those with limited training data or significant linguistic divergence from English. This study systematically investigates how training data, language proximity, and language family affect information loss in multilingual translation. We evaluate two large language models, GPT-4 and Llama 2, by performing round-trip translations. Translation quality was assessed using BLEU scores and BERT similarity metrics. Our results reveal a robust interaction between training data size and language distance: while abundant training data can mitigate the effects of linguistic divergence, languages structurally closer to English consistently yield higher translation quality in low-resource conditions. Among various distance metrics, orthographic, phylogenetic, syntactic, and geographical distances emerge as strong predictors of translation performance. Language family also exerts an independent influence. These findings contribute to a deeper understanding of the linguistic constraints shaping multilingual translation in large language models, emphasizing that translation quality is shaped not only by data volume but also by structural and typological relationships between languages.
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。
