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Investigating Cultural Alignment of Large Language Models
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The intricate relationship between language and culture has long been a subject of exploration within the realm of linguistic anthropology. Large Language Models (LLMs), promoted as repositories of collective human knowledge, raise a pivotal question: do these models genuinely encapsulate the diverse knowledge adopted by different cultures? Our study reveals that these models demonstrate greater cultural alignment along two dimensions -- firstly, when prompted with the dominant language of a specific culture, and secondly, when pretrained with a refined mixture of languages employed by that culture. We quantify cultural alignment by simulating sociological surveys, comparing model responses to those of actual survey participants as references. Specifically, we replicate a survey conducted in various regions of Egypt and the United States through prompting LLMs with different pretraining data mixtures in both Arabic and English with the personas of the real respondents and the survey questions. Further analysis reveals that misalignment becomes more pronounced for underrepresented personas and for culturally sensitive topics, such as those probing social values. Finally, we introduce Anthropological Prompting, a novel method leveraging anthropological reasoning to enhance cultural alignment. Our study emphasizes the necessity for a more balanced multilingual pretraining dataset to better represent the diversity of human experience and the plurality of different cultures with many implications on the topic of cross-lingual transfer.
Forward citations
Cited by 7 Pith papers
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LKValues: Aligning Large Language Models with Sri Lankan Societal Values
A survey-derived Sri Lankan value alignment suite (LKValues) with 150k instruction instances and a 1k benchmark improves Qwen-family LLMs' Sri Lankan value judgment in Sinhala and English.
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The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs
The full text builds the MENA Values benchmark (864 questions, 7 models) and reports that LLM cultural answers shift with language, decline with reasoning prompts, and hide strong internal preferences behind refusals—...
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Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation
An evaluation of five VLMs on culturally-prompted multimodal story generation finds measurable cultural adaptation alongside metric bias and inverse alignment in some models.
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Prompt Programming for Cultural Bias and Alignment of Large Language Models
Automatically optimized prompts (DSPy) reduce survey-measured cultural distance for open-weight LLMs more often than manual cultural prompting, with MIPROv2 and a large proposer model giving the most consistent gains.
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Speaking images. A novel framework for the automated self-description of artworks
A four-stage open-source AI pipeline turns a digitized artwork into a short video where a depicted person animates and narrates the scene.
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Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise
A multi-agent router that selects culturally specialized LLM personas reports a jump in self-scored cultural alignment from 0.208 to 0.820, but the metric and the claimed method are not independently validated.
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LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs
Under a shared retrieval-augmented memory, multiple LLMs' outputs converge to near-identical semantic answers, and the analogous Gaussian mixture system is proven to collapse.
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