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Bilingual AI models show hidden state differences despite embedding alignment

By PulseAugur Editorial · [2 sources] ·

A new research paper titled "Double Trouble: Bilingual Pretraining Leaves Language-Conditioned Effects in Shared-Language Representations" highlights a critical flaw in comparing multilingual language models. Researchers found that while aligning the embedding spaces of English-only and bilingual models can make token embeddings appear similar, the deeper hidden states used for prediction do not align. This discrepancy, observed across eight diverse languages, suggests that embedding alignment can mask significant internal representational differences within models, impacting the reliability of cross-lingual transfer and interpretability studies. AI

IMPACT Highlights potential inaccuracies in cross-lingual model comparisons, impacting research on interpretability and transfer learning.

RANK_REASON The cluster contains a research paper detailing findings about multilingual language models.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Bilingual AI models show hidden state differences despite embedding alignment

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The cluster contains a research paper detailing findings about multilingual language models.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos ·

    Double Trouble: Bilingual Pretraining Leaves Language-Conditioned Effects in Shared-Language Representations

    arXiv:2608.26576v1 Announce Type: new Abstract: When researchers compare multilingual models for probing, interpretability, or cross-lingual transfer, they often align embedding spaces and assume that shared-language representations are comparable. We show that this assumption ca…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Double Trouble: Bilingual Pretraining Leaves Language-Conditioned Effects in Shared-Language Representations

    When researchers compare multilingual models for probing, interpretability, or cross-lingual transfer, they often align embedding spaces and assume that shared-language representations are comparable. We show that this assumption can be premature for decoder-only models. We pretr…

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