CultureForest: Understanding and Evaluating Cultural Norm Grounded Reasoning in LLMs

• URL: http://arxiv.org/abs/2606.01879v1
• Date: Mon, 01 Jun 2026 08:25:12 GMT
• Title: CultureForest: Understanding and Evaluating Cultural Norm Grounded Reasoning in LLMs
• Abstract summary: We introduce CultureForest, a benchmark for textitCultural Norm Grounded Reasoning<n>CultureForest comprises 5,378 examples across 8 domains and 53 countries/regions.<n>Extensive experiments reveal that even top-tier models degrade substantially in open-ended settings.
• Score: 59.09971492475217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing research largely reduces cultural intelligence in LLMs to a knowledge-level problem, overlooking whether models can effectively utilize their acquired knowledge in realistic scenarios. To bridge this gap, we introduce CultureForest, a benchmark for \textit{Cultural Norm Grounded Reasoning}. Each question is grounded in a small set of atomic norms, enabling verifiable and attributable evaluation. CultureForest comprises 5,378 examples across 8 domains and 53 countries/regions, and supports a progressive evaluation from multiple-choice to open-ended generation. Extensive experiments reveal that even top-tier models degrade substantially in open-ended settings, accompanied by pronounced cross-region disparities. Through targeted analysis, we uncover several consistent patterns: (1) test-time reasoning yields limited gains and may exacerbate inequity; (2) models exhibit highly shared regional preference structures; (3) model responses are markedly conservative, especially under stricter cultural constraints; and (4) by disentangling cultural knowledge acquisition from cultural reasoning, we show that while LLMs possess substantial cultural knowledge, their performance is further bottlenecked by its effective use. These findings point to a necessary shift from knowledge-centric evaluation toward measuring knowledge-grounded reasoning.

Related papers

• LLMs Infer Cultural Context but Fail to Apply It When Responding [17.31205603254047]
  We introduce Cultural and Pragmatic Response Inference (CAPRI), a dataset of conversations with varying levels of cultural cues.<n> Experiments with state-of-the-art LLMs show that models can infer cultural background and recall relevant conventions, but often fail to utilize the information.<n>We find that models increasingly adapt their answers as cultural cues accumulate, but their priors are not culture-neutral, sometimes aligning with the model's country of origin.
  arXiv  Detail & Related papers  (2026-06-16T08:53:24Z)
• CURE: Cultural Understanding and Reasoning Evaluation - A Framework for "Thick" Culture Alignment Evaluation in LLMs [24.598338950728234]
  Large language models (LLMs) are increasingly deployed in culturally diverse environments.<n>Existing methods focus on de-contextualized correctness or forced-choice judgments.<n>We introduce a set of benchmarks that present models with realistic situational contexts.
  arXiv  Detail & Related papers  (2025-11-15T03:39:13Z)
• Evaluating and Improving Cultural Awareness of Reward Models for LLM Alignment [38.24188183584244]
  Reward models (RMs) are crucial for aligning large language models with diverse cultures.<n>Existing RM evaluations fall short in assessing cultural awareness due to the scarcity of culturally relevant datasets.<n>We propose Cultural Awareness Reward modeling Benchmark (CARB), covering 10 distinct cultures across 4 cultural domains.
  arXiv  Detail & Related papers  (2025-09-26T02:56:06Z)
• 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)
• From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs [62.9861554207279]
  Adapting cultural values in Large Language Models (LLMs) presents significant challenges.<n>Prior work primarily aligns LLMs with different cultural values using World Values Survey (WVS) data.<n>We investigate WVS-based training for cultural value adaptation and find that relying solely on survey data cane cultural norms and interfere with factual knowledge.
  arXiv  Detail & Related papers  (2025-05-22T09:00:01Z)
• CAReDiO: Cultural Alignment of LLM via Representativeness and Distinctiveness Guided Data Optimization [50.90288681622152]
  Large Language Models (LLMs) more deeply integrate into human life across various regions.<n>Existing approaches develop culturally aligned LLMs through fine-tuning with culture-specific corpora.<n>We introduce CAReDiO, a novel cultural data construction framework.
  arXiv  Detail & Related papers  (2025-04-09T13:40:13Z)
• Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs [7.802103248428407]
  We identify and test three assumptions behind current survey-based evaluation methods.<n>We find a high level of instability across presentation formats, incoherence between evaluated versus held-out cultural dimensions, and erratic behavior under prompt steering.
  arXiv  Detail & Related papers  (2025-03-11T17:59:53Z)
• Culture is Not Trivia: Sociocultural Theory for Cultural NLP [10.76392030245232]
  We argue that these methodological limitations are symptomatic of a theoretical gap.<n>We draw on a well-developed theory of culture from sociocultural linguistics to fill this gap.
  arXiv  Detail & Related papers  (2025-02-17T17:25:11Z)
• Methodology of Adapting Large English Language Models for Specific Cultural Contexts [10.151487049108626]
  We propose a rapid adaptation method for large models in specific cultural contexts. The adapted LLM significantly enhances its capabilities in domain-specific knowledge and adaptability to safety values.
  arXiv  Detail & Related papers  (2024-06-26T09:16:08Z)
• 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.