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The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling

A recent paper published on arXiv explores the internal mechanisms of large language models (LLMs) and their ability to learn multiple languages. The authors utilize new tools from mechanistic interpretability to investigate whether LLMs’ internal structure reflects the linguistic structures of the languages they’re trained on. They focus on two key questions:

  1. When two languages employ the same morphosyntactic processes (e.g., forming a past tense verb), do LLMs use the same internal circuitry to handle them?
  2. When two languages require different morphosyntactic processes, do LLMs use distinct internal circuitry?

To answer these questions, the researchers analyze English and Chinese multilingual and monolingual models, examining their performance on two distinct tasks: indirect object identification (IOI) and past tense verb generation.

The IOI task involves identifying the indirect object in sentences like “Susan and Mary went to the bar. Susan gave a drink to [BLANK].” The authors find that both multilingual and monolingual models utilize similar internal circuitry to solve this task, regardless of the language they’re trained on. This suggests that LLMs can leverage shared mechanisms to handle similar linguistic processes across languages.

In contrast, the past tense verb generation task requires different morphosyntactic processes in English and Chinese. English uses suffixes to mark tense, while Chinese employs a different system. The researchers find that multilingual models rely on largely overlapping circuits for both languages, but also employ language-specific components when necessary. Specifically, the multilingual model uses a circuit with attention heads for both languages, but utilizes feed-forward networks in English to handle morphological marking.

Further, they find that monolingual models trained on English and Chinese independently also adopt nearly the same circuitry for the IOI task, showcasing surprising consistency in how LLMs learn to handle such processes. However, the monolingual models exhibit language-specific circuitry for the past tense task, showcasing the LLM’s ability to adapt to different linguistic structures.

This study sheds light on the internal mechanisms of LLMs and their ability to learn and represent diverse languages. It highlights the trade-off between exploiting common structures and preserving linguistic differences when training LLMs for multiple languages. Understanding these complexities is crucial for building efficient and effective multilingual language technologies. The researchers conclude that further investigation is necessary to explore the extent to which LLMs can generalize their ability to learn linguistic structures across diverse languages.