Breaking the Language Barrier: Can Direct Inference Outperform
  Pre-Translation in Multilingual LLM Applications?
Yotam Intrator Matan Halfon Roman Goldenberg Reut Tsarfaty Matan Eyal Ehud Rivlin Yossi Matias Natalia Aizenberg
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.