How Bilingual Language Models Learn Shared Grammatical Structures

The ability of language models to transfer knowledge between languages is essential for their multilingual competence. However, exactly how this transfer works is still a subject of research. A new study investigates what happens to a monolingual language model when it begins to learn a second language. The focus is on the emergence of shared, multilingual representations within the model.

To answer this question, researchers trained small bilingual models, controlling the amount of training data for each language as well as the order of language presentation. The method used to investigate the grammatical representations was structural priming, a procedure also used in psycholinguistics to study human language processing.

Structural priming is based on the observation that producing or understanding a certain grammatical structure increases the likelihood of subsequently using a similar structure. This phenomenon is interpreted as an indication of the activation of shared grammatical representations.

The study first replicated known results on cross-lingual structural priming. It showed that after controlling for the amount of training data and language presentation, asymmetric effects occur between language pairs and language directions. This asymmetry, the authors argue, could also be relevant for the interpretation of structural priming effects in humans.

Furthermore, the researchers found that structural priming effects are weaker in less similar language pairs. This points to potential limits of cross-lingual transfer and shared representations for typologically different languages. The results underscore the importance of language similarity for the success of knowledge transfer.

Implications for the Development of Language Models

The findings of this study are relevant for the development and optimization of multilingual language models. A better understanding of the mechanisms of cross-lingual transfer can help increase the efficiency of training and improve the performance of models in different languages.

For companies like Mindverse, which specialize in the development of customized AI solutions, these research results are particularly important. The development of chatbots, voicebots, AI search engines, and knowledge systems benefits from a deeper understanding of how multilingual language models work.

Optimizing cross-lingual transfer makes it possible to use resources more efficiently and reduce development costs for multilingual AI applications. At the same time, a better understanding of the underlying processes can lead to the creation of more robust and reliable systems.

Research in this area is still in its early stages, and further studies are needed to fully decipher the complex relationships of cross-lingual transfer. However, this study provides important insights that pave the way for future developments.