Challenge: Large language models (LLMs) are adept at question answering and reasoning tasks, but when reasoning in situational context, human expectations vary depending on the relevant cultural common ground.
Approach: They construct and evaluate a dataset for proverb understanding with conversational context for six different languages and their usage within the context.
Outcome: The proposed model is able to reason with proverbs and sayings in conversational contexts.

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Challenge: Recent research has demonstrated that large language models (LLMs) can translate cultural elements in languages such as idioms and proverbs.
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A Survey of Multilingual Reasoning in Language Models (2025.findings-emnlp)

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Challenge: This survey provides the first in-depth review of multilingual reasoning in Language Models.
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JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking (2025.naacl-long)

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Challenge: Recent advances in instruction fine-tuning and alignment methods have enhanced the adaptability of large language models to user preferences.
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Disentangling Language and Culture for Evaluating Multilingual Large Language Models (2025.acl-long)

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Challenge: Extensive evaluations of large language models (LLMs) are conducted on a wide range of models, revealing a notable cultural-linguistic synergy phenomenon, where models exhibit better performance when questions are culturally aligned with the language.
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Towards Robust Knowledge Representations in Multilingual LLMs for Equivalence and Inheritance based Consistent Reasoning (2025.naacl-long)

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Challenge: Recent advances in Large Language Models have led to impressive linguistic capabilities and emergent reasoning behaviors.
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Memorization or Reasoning? Exploring the Idiom Understanding of LLMs (2025.emnlp-main)

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Challenge: idioms have long posed a challenge due to their unique linguistic properties, which set them apart from other common expressions.
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ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding (2025.findings-naacl)

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Challenge: Large language models (LLMs) evaluation is gaining increasing attention as they are typically trained on general-domain datasets while demonstrating notable performance on tasks out of their training domains.
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Eliciting Better Multilingual Structured Reasoning from LLMs through Code (2024.acl-long)

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Challenge: xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks.
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Towards Practical and Knowledgeable LLMs for a Multilingual World: A Thesis Proposal (2025.naacl-srw)

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Challenge: a proposed thesis examines the role that multilinguality occupies in the development of practical and knowledgeable LLMs.
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Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues (2026.acl-long)

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Challenge: Most benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking cultural nuances that naturally arise in dialogues.
Approach: They propose a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both Modern Standard Arabic (MSA) and each country’s respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics.
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