The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling
Ruochen Zhang, Qinan Yu, Matianyu Zang, Carsten Eickhoff, Ellie Pavlick
2024-10-15
Summary
This paper discusses how large language models (LLMs) understand and process different languages, focusing on whether they use similar structures for languages that share grammatical rules and whether they adapt to different rules when necessary.
What's the problem?
While LLMs have improved in handling multiple languages, there is still a lack of understanding about how these models manage the similarities and differences between languages. Specifically, researchers want to know if LLMs use the same internal mechanisms for similar grammatical processes across different languages or if they switch to different mechanisms for distinct processes.
What's the solution?
The authors analyze how LLMs, specifically comparing models trained on English and Chinese, handle various linguistic tasks. They find that when two languages share the same grammatical processes, the models use the same internal pathways, even if trained separately. However, when languages require different grammatical structures, the models adapt by using specific components designed for those languages. This research helps clarify how LLMs balance commonalities in language while also respecting unique aspects of each language.
Why it matters?
This research is important because it enhances our understanding of how multilingual models work, which can lead to better AI systems that are more effective at processing and generating text in multiple languages. By knowing how LLMs utilize both shared and unique structures, developers can improve these models to perform better across diverse linguistic contexts.
Abstract
We employ new tools from mechanistic interpretability in order to ask whether the internal structure of large language models (LLMs) shows correspondence to the linguistic structures which underlie the languages on which they are trained. In particular, we ask (1) when two languages employ the same morphosyntactic processes, do LLMs handle them using shared internal circuitry? and (2) when two languages require different morphosyntactic processes, do LLMs handle them using different internal circuitry? Using English and Chinese multilingual and monolingual models, we analyze the internal circuitry involved in two tasks. We find evidence that models employ the same circuit to handle the same syntactic process independently of the language in which it occurs, and that this is the case even for monolingual models trained completely independently. Moreover, we show that multilingual models employ language-specific components (attention heads and feed-forward networks) when needed to handle linguistic processes (e.g., morphological marking) that only exist in some languages. Together, our results provide new insights into how LLMs trade off between exploiting common structures and preserving linguistic differences when tasked with modeling multiple languages simultaneously.