WorldView-Bench: A Benchmark for Evaluating Global Cultural Perspectives in Large Language Models (2505.09595v1)

Abstract: LLMs are predominantly trained and aligned in ways that reinforce Western-centric epistemologies and socio-cultural norms, leading to cultural homogenization and limiting their ability to reflect global civilizational plurality. Existing benchmarking frameworks fail to adequately capture this bias, as they rely on rigid, closed-form assessments that overlook the complexity of cultural inclusivity. To address this, we introduce WorldView-Bench, a benchmark designed to evaluate Global Cultural Inclusivity (GCI) in LLMs by analyzing their ability to accommodate diverse worldviews. Our approach is grounded in the Multiplex Worldview proposed by Senturk et al., which distinguishes between Uniplex models, reinforcing cultural homogenization, and Multiplex models, which integrate diverse perspectives. WorldView-Bench measures Cultural Polarization, the exclusion of alternative perspectives, through free-form generative evaluation rather than conventional categorical benchmarks. We implement applied multiplexity through two intervention strategies: (1) Contextually-Implemented Multiplex LLMs, where system prompts embed multiplexity principles, and (2) Multi-Agent System (MAS)-Implemented Multiplex LLMs, where multiple LLM agents representing distinct cultural perspectives collaboratively generate responses. Our results demonstrate a significant increase in Perspectives Distribution Score (PDS) entropy from 13% at baseline to 94% with MAS-Implemented Multiplex LLMs, alongside a shift toward positive sentiment (67.7%) and enhanced cultural balance. These findings highlight the potential of multiplex-aware AI evaluation in mitigating cultural bias in LLMs, paving the way for more inclusive and ethically aligned AI systems.

Evaluating Cultural Inclusivity with WorldView-Bench

The paper introduces WorldView-Bench, a benchmark specifically designed to evaluate the global cultural inclusivity of LLMs. LLMs, while demonstrating advanced capabilities across numerous applications, are often trained in environments that reflect a predominantly Western-centric perspective. The authors of this paper highlight that existing benchmarking frameworks inadequately assess cultural bias or the ability of LLMs to represent diverse worldviews, primarily due to their closed-form, rigid evaluation techniques. The research provides a crucial tool for analyzing these biases through a more nuanced and open-form method.

Overview of WorldView-Bench

WorldView-Bench aims to address the cultural biases overflowing from the predominant Western epistemologies in LLMs. The authors leverage the concept of Multiplexity, which was earlier proposed by Senturk et al., asserting that LLMs today often embody 'uniplex' tendencies, favoring assimilation to dominant cultural narratives. In contrast, Multiplex analysis aims to incorporate diverse perspectives, ensuring a balanced representation across different cultural contexts. This difference is quantitatively evaluated using two innovative metrics developed in this paper: the Perspectives Distribution Score (PDS) and its Entropy. These measures capture the breadth of cultural inclusivity in LLM outputs through an analysis that inherently resists oversimplification by predefined categories.

Methodological Contributions

The authors present a comprehensive methodological framework built around WorldView-Bench. The benchmark consists of 175 diverse questions across seven knowledge domains, probing cultural adaptability, inclusivity, and ethical sensitivity. The evaluation pipeline proceeds through several stages: using zero-shot classification for response characterization, metrics-based analysis for inclusivity quantification, and sentiment analysis to detect implicit cultural stances. The paper emphasizes that this open-ended evaluation captures more profound insights related to cultural dimensions than traditional benchmarks.

Two strategies for fostering multiplexity within LLMs are tested. The first—Contextually-Implemented Multiplex LLMs—utilizes system prompts that embed multiplex principles, guiding the LLMs in generating more culturally inclusive responses. The second—Multi-Agent System (MAS) Implemented Multiplex LLMs—engages multiple LLM agents representing distinct cultural lenses and collaboratively synthesizes their perspectives into a final, combined output. The results of the latter strategy are particularly effective, dramatically increasing the PDS Entropy from 13% at the baseline to 94%.

Implications and Future Directions

The implications of this work are substantial for the field of AI, particularly for those researching cultural bias and inclusivity in LLMs. The proposed benchmark and methodologies offer pathways to develop AI systems that are not only more inclusive but also more ethically aligned with diverse cultural settings. The significant improvement in the PDS score with the MAS approach showcases a promising direction for future research. By leveraging collaborative agent models that represent multiple cultures, researchers can achieve a more balanced, holistic AI performance that better reflects societal plurality.

The research suggests several avenues for future investigation, including the refinement of multiplexive models to incorporate even broader cultural datasets and develop real-time assessments of cultural inclusivity during LLM training stages. Researchers might also explore whether the benchmarking framework can be extended to other AI domains or integrated with ongoing fine-tuning processes to continuously improve model inclusivity and cultural sensitivity.

In conclusion, WorldView-Bench represents a significant step forward in understanding and enhancing the cultural inclusivity of LLMs. By shedding light on the often-overlooked nuances of global cultural representation in AI, the paper offers a robust framework for future explorations and practical applications in creating culturally aware AI systems.