Bilingual Language Models: A Case of Lingual Identity Crisis?

For bilingual speakers, reading is a complex dance of languages. Words that look the same in different languages can either aid or confuse. In the space of AI, bilingual language models are tasked with mirroring this intricate mental juggle. But can they really?

Experimenting with Language Models

Researchers trained Dutch-English causal Transformers under various conditions to test this very question. They manipulated vocabulary-sharing scenarios to see if words identified as 'friends' or 'false friends', those that look the same across languages but mean different things, would lead to similar effects in AI as they do in human brains.

Typically, cognates, or 'friends', help comprehension, while interlingual homographs, or 'false friends', muddy the waters. The team used data from psycholinguistic studies, evaluating these models with surprisal and embedding similarity analyses.

Results: A Mixed Bag

The findings are intriguing. These models, in most cases, maintain a separation between languages, except when embeddings are shared. In those instances, both friends and false friends exhibited facilitation compared to controls. However, the real kicker was that these effects were driven more by frequency than by the consistency of form-meaning mappings.

It seems that unless embeddings are exclusively shared for friends, the qualitative patterns observed in human bilinguals aren't fully replicated. This raises a critical question: Just how well do these models understand the nuances of bilingual processing?

What They're Not Telling You

While these models capture some aspects of cross-linguistic activation, their alignment with human processing is tenuous at best. What they're not telling you is that how lexical overlap is encoded may critically limit their explanatory power. If these models don't accurately capture the bilingual experience, how relevant are they for practical applications?

I've seen this pattern before, where models tout their ability to replicate human behavior but fall short when scrutinized. It's a reminder that while AI can achieve impressive feats, it still struggles with the complexity of real-world human cognition.

In a world that increasingly relies on AI for language processing, this raises a critical point: Should we be skeptical of models that promise too much without fully delivering? The claim doesn't survive scrutiny.