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Bilingual AI models show hidden state mismatch despite embedding alignment

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a significant issue in how bilingual language models are evaluated, particularly decoder-only models. When embedding spaces are aligned to compare models trained on different language sets, the deeper hidden states used for prediction do not show similar alignment. This discrepancy persists across various languages and control methods, indicating that embedding alignment can mask fundamental differences in internal language representation. The study suggests this mismatch arises during contextual processing, impacting the reliability of downstream studies that assume interchangeable aligned models. AI

IMPACT Highlights a critical flaw in evaluating bilingual AI models, potentially impacting cross-lingual transfer and interpretability research.

RANK_REASON The cluster contains a research paper published on arXiv detailing findings about language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Bilingual AI models show hidden state mismatch despite embedding alignment

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The cluster contains a research paper published on arXiv detailing findings about language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  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…

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