The Obscure Limitation of Modular Multilingual Language Models
Abstract
The addition of language identification to modular multilingual language models enhances performance in real-world multilingual scenarios by addressing the limitations caused by their pipelined approach.
We expose the limitation of modular multilingual language models (MLMs) in multilingual inference scenarios with unknown languages. Existing evaluations of modular MLMs exclude the involvement of language identification (LID) modules, which obscures the performance of real-case multilingual scenarios of modular MLMs. In this work, we showcase the effect of adding LID on the multilingual evaluation of modular MLMs and provide discussions for closing the performance gap of caused by the pipelined approach of LID and modular MLMs.
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