Cross-lingual evaluation framework reveals LLMs redistribute cultural narrative structure while preserving semantic meaning
A new arXiv preprint introduces a multilingual evaluation framework using 414 proverbs across 15 languages to assess whether LLMs preserve culturally grounded meaning when generating narratives. Using four LLMs to produce 13k narratives, the study finds that cross-lingual prompting preserves proverb-level semantic meaning but systematically redistributes agency, social positioning, and narrative structure. Strong inter-model convergence across architectures suggests multilingual LLMs rely on shared semantic abstractions. The authors argue that semantic similarity metrics alone overestimate cultural preservation in multilingual evaluations.
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Study finds local languages provide better cultural knowledge access in LLMs once proficiency is controlled
A new arXiv paper introduces a controlled evaluation framework to disentangle language proficiency from culture-specific knowledge access in LLMs. Using real-world cultural questions across 13 locales and ~80 models, the authors apply item response theory to show that while English dominates on culture-agnostic questions, local languages yield a consistent knowledge-access advantage on culture-specific questions once proficiency differences are factored out. The finding challenges the common interpretation that weaker local-language accuracy implies weaker cultural knowledge, and has implications for how multilingual and regionally-aligned models are evaluated.
Cross-lingual prompting strategies unlock hidden parametric knowledge in LLMs
A new arXiv preprint investigates how cross-lingual prompting can surface factual knowledge that standard inference techniques fail to retrieve in multilingual LLMs. The authors identify four dimensions of cross-lingual exploration governing parametric knowledge retrieval and evaluate them on multilingual factual benchmarks across 17 typologically diverse languages. Results show cross-lingual exploration improves both factual recall and cross-lingual consistency, and is claimed to be a more compute-efficient approach than scaling native-language inference.
Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study
This paper systematically investigates strategies for extending LLM-based automatic evaluation (LLMs-as-a-Judge) to multilingual settings, covering high-, mid-, and low-resource languages (English, Spanish, Basque). The authors compare instruction translation, monolingual vs. multilingual supervision, and model size, finding that fine-tuned smaller models can match proprietary models when in-domain data is available, while zero-shot larger models are preferable out-of-domain. Two meta-evaluation datasets are extended to Spanish and Basque, and all data and code are publicly released.
Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora
This paper investigates whether LLM-based machine translation can preserve moral semantic content well enough to enable cross-lingual moral values classification, using Polish as a test case with ~50k annotated social media posts. A four-method validation pipeline (LaBSE embedding similarity, CKA, LLM-as-judge, and classifier parity) shows mean cosine similarity of 0.86 and AUC gaps of only 0.01–0.02 across Moral Foundations categories. The results suggest machine translation is a practical path to extending moral values NLP research to under-resourced languages, with expected generalization to related Slavic languages.
LLMs fail to consistently simulate demographic perspective-taking in hate speech annotation
A new arXiv paper evaluates whether persona-conditioned LLMs can replicate how different demographic groups perceive hate speech, testing three dimensions: inter-group disagreement, in-group sensitivity, and vicarious prediction. No model consistently captures all three dimensions, and performance is highly model-dependent rather than emerging reliably from identity prompts alone. Vicarious prompting with Llama 3.1 provides the closest approximation to human disagreement patterns across demographic axes. The findings have implications for using LLMs as proxies for diverse human annotators in content moderation tasks.
Tracing the Emergence of Human-Like Pragmatic Reasoning in LLMs Across Languages
Researchers conducted a population-matching experiment evaluating 25 LLMs on conditional inference tasks across four languages, comparing model behavior to matched human populations. The study finds that LLMs function as accurate semantic operators but systematically fail to capture pragmatic enrichments—context-sensitive inferences beyond literal logical meaning—that humans apply effortlessly. Model performance on pragmatic reasoning is not predicted by open vs. closed weights, training orientation, or architecture type, suggesting pragmatic reasoning remains an emergent and unreliable capability. The findings contribute to ongoing debates about whether LLMs reason like humans or merely approximate surface-level linguistic patterns.
Quantifying Cross-Linguistic Effects of Syncretism on Agreement Attraction Using LLM Processing Proxies
This paper investigates why morphological syncretism amplifies agreement attraction errors in some languages (English, German, Russian) but not others (Turkish, Armenian), a pattern lacking a principled account. The authors use surprisal and attention entropy derived from large language models as proxies for human sentence processing across four languages. LLM-derived measures successfully replicate behavioral findings in English and German, align with Turkish null results, and partially capture Russian patterns. The work demonstrates LLMs as tools for cross-linguistic psycholinguistic investigation.
C4STYLI Benchmark: Probing Cultural Aesthetic Stylistics Awareness in LLMs
Researchers introduce C4STYLI, a benchmark of stylized translated movie titles and advertising slogans from Hong Kong and mainland China, designed to evaluate LLMs on cross-cultural aesthetic stylistics. Evaluations reveal that LLMs diverge from human stylistic recognition, with recognition ability varying by text domain and not consistently predicting generation performance. Structural ablation using logistic regression probes shows that LLMs in the Hong Kong setting rely on surface-level linguistic cues rather than deeper stylistic structure, indicating limited cultural sensitivity.