Multilingual models invert sentiment polarity 28.7% of the time on Bengali text and show asymmetric affective weighting plus a 57% rise in error on formal dialect compared with colloquial Bengali.
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2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Modifying nationality and language parameters in English-centric personas for mental health dialogues introduces clinical inconsistencies across languages and causes LLM judges to perform inaccurately on non-English depression severity assessments.
citing papers explorer
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Cross-Lingual Sentiment Misalignment: Auditing Multilingual Language Models for Inversion Risk, Dialectal Representation, and Affective Stability
Multilingual models invert sentiment polarity 28.7% of the time on Bengali text and show asymmetric affective weighting plus a 57% rise in error on formal dialect compared with colloquial Bengali.
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Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language
Modifying nationality and language parameters in English-centric personas for mental health dialogues introduces clinical inconsistencies across languages and causes LLM judges to perform inaccurately on non-English depression severity assessments.