LLMs default to U.S. frameworks for English prompts and China frameworks for Chinese prompts on jurisdiction-underspecified legal-administrative queries, with the pattern holding across all seven tested models.
Computational Linguistics , volume =
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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ExCAM is a new explainable metric for cultural awareness in LLMs, trained on the ExCAM40k dataset derived from nine existing benchmarks with added synthetic errors, achieving up to 80% error detection accuracy.
Occupational prompting of open-weight LLMs elicits structured value patterns in Inglehart-Welzel cultural space, extending prior nationality-based cultural bias evaluations.
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.
citing papers explorer
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Which Institutional Frameworks Do Chatbots Assume? Auditing Jurisdictional Defaults in Multilingual LLMs
LLMs default to U.S. frameworks for English prompts and China frameworks for Chinese prompts on jurisdiction-underspecified legal-administrative queries, with the pattern holding across all seven tested models.
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ExCAM: Explainable Cultural Awareness Metrics
ExCAM is a new explainable metric for cultural awareness in LLMs, trained on the ExCAM40k dataset derived from nine existing benchmarks with added synthetic errors, achieving up to 80% error detection accuracy.
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Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.