Towards A Cultural Intelligence and Values Inferences Quality Benchmark for Community Values and Common Knowledge

2025-12-04

AI Summary

Current large language models (LLMs) predominantly encode Western mainstream cultural narratives, limiting their alignment with the values and commonsense knowledge of diverse U.S. populations—particularly marginalized communities. Existing national-level alignment benchmarks (e.g., KorNAT) lack granular, community-level representativeness. To address this, we introduce CIVIQ: the first cultural intelligence evaluation benchmark explicitly designed to assess LLM alignment with community-level social values and culturally situated commonsense reasoning. Methodologically, CIVIQ transcends nation-scale abstractions by integrating qualitative ethnographic research and social computing to construct a cross-cultural transfer framework. It employs localized data collection and culturally sensitive annotation to build a multiracial, intergenerational, and geographically diverse evaluation dataset. CIVIQ provides a reusable, methodologically grounded toolkit for developing, evaluating, and iteratively refining culturally aware LLMs—thereby advancing concrete, practice-oriented progress in AI fairness and inclusion.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Large language models (LLMs) have emerged as a powerful technology, and thus, we have seen widespread adoption and use on software engineering teams. Most often, LLMs are designed as "general purpose" technologies meant to represent the general population. Unfortunately, this often means alignment with predominantly Western Caucasian narratives and misalignment with other cultures and populations that engage in collaborative innovation. In response to this misalignment, there have been recent efforts centered on the development of "culturally-informed" LLMs, such as ChatBlackGPT, that are capable of better aligning with historically marginalized experiences and perspectives. Despite this progress, there has been little effort aimed at supporting our ability to develop and evaluate culturally-informed LLMs. A recent effort proposed an approach for developing a national alignment benchmark that emphasizes alignment with national social values and common knowledge. However, given the range of cultural identities present in the United States (U.S.), a national alignment benchmark is an ineffective goal for broader representation. To help fill this gap in this US context, we propose a replication study that translates the process used to develop KorNAT, a Korean National LLM alignment benchmark, to develop CIVIQ, a Cultural Intelligence and Values Inference Quality benchmark centered on alignment with community social values and common knowledge. Our work provides a critical foundation for research and development aimed at cultural alignment of AI technologies in practice.

Problem

• Develops a benchmark for evaluating culturally-informed large language models.
• Addresses misalignment of AI with non-Western cultural narratives and values.
• Focuses on community-level social values and common knowledge in the U.S.

Innovation

• Develop culturally-informed LLMs like ChatBlackGPT
• Translate Korean benchmark method to create CIVIQ
• Focus on community values instead of national alignment