Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs

• URL: http://arxiv.org/abs/2604.04732v1
• Date: Mon, 06 Apr 2026 15:00:05 GMT
• Title: Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs
• Abstract summary: Large language models (LLMs) are often described as multilingual because they can understand and respond in many languages.<n>This paper presents a preliminary computational audit of cultural inclusivity in a creative writing task.
• Score: 15.854854496840074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are often described as multilingual because they can understand and respond in many languages. However, speaking a language is not the same as reasoning within a culture. This distinction motivates a critical question: do LLMs truly conduct culture-aware reasoning? This paper presents a preliminary computational audit of cultural inclusivity in a creative writing task. We empirically examine whether LLMs act as culturally diverse creative partners or merely as cultural translators that leverage a dominant conceptual framework with localized expressions. Using a metaphor generation task spanning five cultural settings and several abstract concepts as a case study, we find that the model exhibits stereotyped metaphor usage for certain settings, as well as Western defaultism. These findings suggest that merely prompting an LLM with a cultural identity does not guarantee culturally grounded reasoning.

Related papers

• LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction [57.23766971626989]
  Large language models (LLMs) encode rich cultural knowledge learned from diverse web-scale data.<n>We present an iterative, prompt-based framework for constructing a Cultural Commonsense Knowledge Graph (CCKG)<n>We find that the cultural knowledge graphs are better realized in English, even when the target culture is non-English.
  arXiv  Detail & Related papers  (2026-01-25T20:05:04Z)
• Do Large Language Models Truly Understand Cross-cultural Differences? [53.481048019144644]
  We develop a scenario-based benchmark to evaluate large language models' cross-cultural understanding and reasoning.<n>Grounded in cultural theory, we categorize cross-cultural capabilities into nine dimensions.<n>The dataset supports continuous expansion, and experiments confirm its transferability to other languages.
  arXiv  Detail & Related papers  (2025-12-08T01:21:58Z)
• CultureScope: A Dimensional Lens for Probing Cultural Understanding in LLMs [57.653830744706305]
  CultureScope is the most comprehensive evaluation framework to date for assessing cultural understanding in large language models.<n>Inspired by the cultural iceberg theory, we design a novel dimensional schema for cultural knowledge classification.<n> Experimental results demonstrate that our method can effectively evaluate cultural understanding.
  arXiv  Detail & Related papers  (2025-09-19T17:47:48Z)
• Designing LLMs for cultural sensitivity: Evidence from English-Japanese translation [31.55428098109144]
  Large language models (LLMs) are increasingly used in everyday communication.<n>We analyze the cultural sensitivity of different LLM designs when applied to English-Japanese translations of workplace e-mails.<n>We find that culturally-tailored prompting can improve cultural fit.
  arXiv  Detail & Related papers  (2025-09-15T13:37:35Z)
• Entangled in Representations: Mechanistic Investigation of Cultural Biases in Large Language Models [42.367959511140036]
  We propose Culturescope, the first interpretability-based method that probes the internal representations of large language models.<n>We introduce a cultural flattening score as a measure of the intrinsic cultural biases.<n>Our experimental results reveal that LLMs encode Western-dominance bias and cultural flattening in their cultural knowledge space.
  arXiv  Detail & Related papers  (2025-08-12T12:05:32Z)
• MultiNRC: A Challenging and Native Multilingual Reasoning Evaluation Benchmark for LLMs [56.87573414161703]
  We introduce the Multilingual Native Reasoning Challenge (MultiNRC), a benchmark to assess Large Language Models (LLMs)<n>MultiNRC covers four core reasoning categories: language-specific linguistic reasoning, wordplay & riddles, cultural/tradition reasoning, and math reasoning with cultural relevance.<n>For cultural/tradition reasoning and math reasoning with cultural relevance, we also provide English equivalent translations of the multilingual questions by manual translation from native speakers fluent in English.
  arXiv  Detail & Related papers  (2025-07-23T12:56:31Z)
• Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition [0.0]
  We introduce Nunchi-Bench, a benchmark designed to evaluate large language models' cultural understanding.<n>The benchmark consists of 247 questions spanning 31 topics, assessing factual knowledge, culturally appropriate advice, and situational interpretation.<n>We evaluate multilingual LLMs in both Korean and English to analyze their ability to reason about Korean cultural contexts.
  arXiv  Detail & Related papers  (2025-07-05T11:52:09Z)
• Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness [11.98067475490853]
  We argue that it is not cultural awareness or knowledge, rather meta-cultural competence, which is required of an AI system.<n>We lay out the principles of meta-cultural competence AI systems, and discuss ways to measure and model those.
  arXiv  Detail & Related papers  (2025-02-09T04:51:59Z)
• Translating Across Cultures: LLMs for Intralingual Cultural Adaptation [12.5954253354303]
  We define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs. We analyze possible issues with automatic adaptation. We hope that this paper will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.
  arXiv  Detail & Related papers  (2024-06-20T17:06:58Z)
• Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense [98.09670425244462]
  Large language models (LLMs) have demonstrated substantial commonsense understanding. This paper examines the capabilities and limitations of several state-of-the-art LLMs in the context of cultural commonsense tasks.
  arXiv  Detail & Related papers  (2024-05-07T20:28:34Z)
• Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models [89.94270049334479]
  This paper identifies a cultural dominance issue within large language models (LLMs) LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages.
  arXiv  Detail & Related papers  (2023-10-19T05:38:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.