Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the widespread practice of pre-translation, i.e., translating non-English inputs to English before inference, leading to complexity and information loss. This study re-evaluates the need for pre-translation in the context of PaLM2 models (Anil et al., 2023), which have been established as highly performant in multilingual tasks. We offer a comprehensive investigation across 108 languages and 6 diverse benchmarks, including open-end generative tasks, which were excluded from previous similar studies. Our findings challenge the pre-translation paradigm established in prior research, highlighting the advantages of direct inference in PaLM2. Specifically, PaLM2-L consistently outperforms pre-translation in 94 out of 108 languages. These findings pave the way for more efficient and effective multilingual applications, alleviating the limitations associated with pre-translation and unlocking linguistic authenticity.

这项研究通过对108种不同语言和6个多样化基准测试进行全面调查，重新评估了PaLM2模型的预翻译需求，并发现PaLM2-L在94种语言中持续优于预翻译，揭示了直接推理在多语言应用中的优势，为更高效、更有效的多语言应用铺平了道路。

突破语言障碍：直接推断是否能在多语言LLM应用中胜过预翻译？