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Cultural Value Differences of LLMs: Prompt, Language, and Model Size

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arxiv 2407.16891 v1 pith:4THFZGET submitted 2024-06-17 cs.CY cs.CL

classification cs.CYcs.CL
keywords culturallanguagevaluesmodeldifferencesllmssamesize
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Our study aims to identify behavior patterns in cultural values exhibited by large language models (LLMs). The studied variants include question ordering, prompting language, and model size. Our experiments reveal that each tested LLM can efficiently behave with different cultural values. More interestingly: (i) LLMs exhibit relatively consistent cultural values when presented with prompts in a single language. (ii) The prompting language e.g., Chinese or English, can influence the expression of cultural values. The same question can elicit divergent cultural values when the same LLM is queried in a different language. (iii) Differences in sizes of the same model (e.g., Llama2-7B vs 13B vs 70B) have a more significant impact on their demonstrated cultural values than model differences (e.g., Llama2 vs Mixtral). Our experiments reveal that query language and model size of LLM are the main factors resulting in cultural value differences.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cash or Comfort? How LLMs Value Your Inconvenience

    cs.CL 2025-06 conditional novelty 6.0 of 10

    LLMs assign inconsistent, wording-sensitive money values to waiting, walking, hunger, and pain, sometimes accepting 1 euro for major inconvenience and rejecting free money for no inconvenience.

  2. A Cross-Cultural Comparison of LLM-based Public Opinion Simulation: Evaluating Chinese and U.S. Models on Diverse Societies

    cs.CL 2025-06 conditional novelty 5.0 of 10

    Across U.S. and Chinese survey questions, DeepSeek, GPT-4o, Qwen2.5, and Llama-3.3 all show demographic overgeneralization, with no consistent home-field advantage for the Chinese model.

  3. Reproducibility Study of "Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents"

    cs.AI 2025-05 conditional novelty 5.0 of 10

    A replication of the GovSim LLM cooperation benchmark confirms that large models sustain shared resources and that a cooperation prompt rescues smaller models; new scenarios show framing and model mix change outcomes.

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