大模型多语言生成失败，主因是翻译环节隐性失效
The Translation Barrier Hypothesis: Multilingual Generation with Large Language Models Suffers from Implicit Translation Failure
- 模型先解任务再翻译答案，翻译阶段易出错
- 108对语言中，多数低资源语言翻译错误占主导
- 适合研究多语言模型瓶颈与改进方向的学者
大型语言模型在多语言生成中，中低资源语言质量较差，但原因尚不明确。我们首次揭示了一个隐式的‘任务求解→翻译’流程：模型先以几乎不依赖目标语言的方式完成任务，再将答案概念翻译至目标语言。我们提出‘翻译屏障假说’，认为即使任务求解成功，翻译阶段的失败也是输出质量差的重要原因。我们在108对语言组合上量化了该流程中各阶段的贡献，发现翻译屏障解释了多数语言对的错误，尤其在低资源目标语言中更为严重。结果揭示了端到端多语言生成的关键瓶颈，对提升大模型多语能力具有重要启示。
原文摘要 · Abstract (English)
Multilingual generation with large language models (LLMs) is often of poor quality for mid- to low-resource languages, but the causes for this are not well-understood. We first demonstrate the existence of an implicit task-solving-->translation pipeline for generation, whereby the model first solves the required task in a largely target-language-agnostic manner, and subsequently translates answer concepts into the intended target language. We hypothesize that the failure of the translation stage, despite task-solving success, is an important culprit for the observed low quality of final outputs, and formalize this as the translation barrier hypothesis. We quantify the extent to which either stage in the pipeline is responsible for final failure for a word translation task across 108 language pairs, and find that the translation barrier explains a dominant portion of error for a majority of language pairs, and is especially severe for low-resource target languages. Our results highlight an important bottleneck for end-to-end multilingual generation, relevant for future work seeking to improve multilinguality in LLMs.
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