Prompt Programming for Cultural Bias and Alignment of Large Language Models

• URL: http://arxiv.org/abs/2603.16827v1
• Date: Tue, 17 Mar 2026 17:34:40 GMT
• Title: Prompt Programming for Cultural Bias and Alignment of Large Language Models
• Abstract summary: Large language models (LLMs) often exhibit cultural biases that misalign with target populations.<n>Previous work introduced a survey-grounded cultural alignment framework and showed that culture-specific prompting can reduce misalignment.<n>This paper reproduces that framework by reproducing its social sciences survey based projection and distance metrics on open-weight LLMs.<n>We introduce use of prompt programming with DSPy for this problem-treating prompts as modular, optimizable programs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that misalign with target populations. As LLMs are increasingly used for strategic decision-making, policy support, and document engineering tasks such as summarization, categorization, and compliance-oriented auditing, improving cultural alignment is important for ensuring that downstream analyses and recommendations reflect target-population value profiles rather than default model priors. Previous work introduced a survey-grounded cultural alignment framework and showed that culture-specific prompting can reduce misalignment, but it primarily evaluated proprietary models and relied on manual prompt engineering. In this paper, we validate and extend that framework by reproducing its social sciences survey based projection and distance metrics on open-weight LLMs, testing whether the same cultural skew and benefits of culture conditioning persist outside closed LLM systems. Building on this foundation, we introduce use of prompt programming with DSPy for this problem-treating prompts as modular, optimizable programs-to systematically tune cultural conditioning by optimizing against cultural-distance objectives. In our experiments, we show that prompt optimization often improves upon cultural prompt engineering, suggesting prompt compilation with DSPy can provide a more stable and transferable route to culturally aligned LLM responses.

Related papers

• CultureForest: Understanding and Evaluating Cultural Norm Grounded Reasoning in LLMs [59.09971492475217]
  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.
  arXiv  Detail & Related papers  (2026-06-01T08:25:12Z)
• Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models [78.19037585302475]
  Large language models (LLMs) are increasingly deployed in culturally sensitive real-world tasks.<n>Existing cultural alignment approaches fail to align LLMs' broad cultural values with the specific goals of downstream tasks.<n>We propose CultureManager, a novel pipeline for task-specific cultural alignment.
  arXiv  Detail & Related papers  (2026-02-25T23:27:18Z)
• 'Too much alignment; not enough culture': Re-balancing cultural alignment practices in LLMs [0.0]
  This paper argues for a shift towards integrating qualitative approaches into AI alignment practices.<n> Drawing inspiration from Clifford Geertz's concept of "thick description," we propose that AI systems must produce outputs that reflect deeper cultural meanings.
  arXiv  Detail & Related papers  (2025-09-30T12:22:53Z)
• 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)
• Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise [0.4711628883579317]
  Current large language models (LLMs) often lack the nuanced understanding required for diverse cultural contexts.<n>We introduce a novel soft prompt fine-tuning framework that enables efficient and modular cultural alignment.
  arXiv  Detail & Related papers  (2025-05-30T21:16:25Z)
• From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test [50.51344198689069]
  We extend the human-centered word association test (WAT) to assess the alignment of large language models with cross-cultural cognition.<n>To address culture preference, we propose CultureSteer, an innovative approach by embedding cultural-specific semantic associations directly within the model's internal representation space.
  arXiv  Detail & Related papers  (2025-05-24T07:05:10Z)
• 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)
• Cultural Learning-Based Culture Adaptation of Language Models [70.1063219524999]
  Adapting large language models (LLMs) to diverse cultural values is a challenging task.<n>We present CLCA, a novel framework for enhancing LLM alignment with cultural values based on cultural learning.
  arXiv  Detail & Related papers  (2025-04-03T18:16:26Z)
• Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs [18.84720654437447]
  A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs)<n>In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches.
  arXiv  Detail & Related papers  (2025-02-12T01:04:13Z)

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.