Examining if English is the Core Processing Language for Multilingual LLMs like Llama 2
Summary: A study by EPFL researchers explores whether multilingual LLMs use English as their internal processing language, focusing on the Llama 2 family.
(AIM)—The question of whether large language models (LLMs) have a “native language” is intriguing, especially when these models are trained on multilingual datasets. This curiosity led researchers from EPFL (École Polytechnique Fédérale de Lausanne) to investigate the internal workings of the Llama 2 family, revealing surprising insights about these models’ linguistic processing.
Investigating LLMs’ Inner Workings
At first glance, one might assume that the native language of an LLM, particularly those trained predominantly on English datasets, would naturally be English. However, the reality is more nuanced, as demonstrated by the research from EPFL, detailed in their paper available on arXiv. The study explores whether models like Llama 2, trained on predominantly English corpora, utilize English as their internal language of thought.
The researchers devised a series of experiments targeting the Llama 2 models. By analyzing the model’s output across various tasks—such as translation, repetition, and cloze tests—they extracted data from all 32 layers of the Llama-2-7B model to understand how it processes non-English inputs and generates outputs.
The Findings
The findings indicate that while the Llama 2 models operate on an abstract conceptual level, their internal representations tend to align more closely with English. For instance, when the model translates a French word to Chinese, its intermediary steps heavily favor English, even though the prompt contains no English words. This phenomenon was evident across multiple tasks, suggesting that Llama 2’s internal “language” is not purely English but a conceptual space with a significant English bias.
The visualized high-dimensional trajectories of token embeddings further support this. Initially, inputs are processed in an abstract conceptual space before mapping back to the output language. Despite this, the intermediate steps show a preference for English-like structures.
Implications and Observations
This research implies that for multilingual LLMs trained on English-dominated datasets, English acts as a de facto conceptual language. This bias could affect the model’s performance in non-English contexts and influence how it interprets and generates text across different languages.
Users have also noticed this bias in practical applications. For example, LLM-generated poetry often doesn’t rhyme in languages other than English, suggesting that the models inherently structure responses with English linguistic patterns in mind.
The EPFL study provides a deeper understanding of the internal workings of multilingual LLMs like Llama 2, highlighting an inherent bias towards English. This insight is crucial for further refining these models and ensuring equitable performance across languages.
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Keywords: Multilingual LLMs, Llama 2, Native Language, AI Research, EPFL, Language Bias, Machine Translation
