Pith. sign in

REVIEW 2 cited by

An Evaluation of Cultural Value Alignment in LLM

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2504.08863 v1 pith:33RGFGFP submitted 2025-04-11 cs.CY cs.AI

classification cs.CYcs.AI
keywords culturalllmsmodelsacrossalignmentoutputalignbetter
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

LLMs as intelligent agents are being increasingly applied in scenarios where human interactions are involved, leading to a critical concern about whether LLMs are faithful to the variations in culture across regions. Several works have investigated this question in various ways, finding that there are biases present in the cultural representations of LLM outputs. To gain a more comprehensive view, in this work, we conduct the first large-scale evaluation of LLM culture assessing 20 countries' cultures and languages across ten LLMs. With a renowned cultural values questionnaire and by carefully analyzing LLM output with human ground truth scores, we thoroughly study LLMs' cultural alignment across countries and among individual models. Our findings show that the output over all models represents a moderate cultural middle ground. Given the overall skew, we propose an alignment metric, revealing that the United States is the best-aligned country and GLM-4 has the best ability to align to cultural values. Deeper investigation sheds light on the influence of model origin, prompt language, and value dimensions on cultural output. Specifically, models, regardless of where they originate, align better with the US than they do with China. The conclusions provide insight to how LLMs can be better aligned to various cultures as well as provoke further discussion of the potential for LLMs to propagate cultural bias and the need for more culturally adaptable models.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters

    cs.CL 2026-01 conditional novelty 6.0 of 10

    A demographic-conditioned mixture of LoRA experts improves LLM cultural alignment and reduces the tendency of dense models to produce generic, averaged responses.

  2. Evaluating LLM Adaptation to Sociodemographic Factors: User Profile vs. Dialogue History

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Most tested LLMs adjust their expressed values to a user's age and education, but consistency between explicit profile and dialogue-history conditions varies across models.

Pith tools