CoCoA: Context-Conditional Cultural Alignment for Large Language Models

2026-08-29

AI Summary

针对大型语言模型中的文化偏见问题，提出CoCoA框架，通过双上下文训练和目标感知梯度调和等方法实现情境条件下的文化对齐。

Abstract

Large Language Models (LLMs) often favor Western-associated entities across cultural contexts. Conventional debiasing methods aim for uniform neutrality, but cultural bias mitigation demands context-conditional behavior, preferring culturally appropriate entities when cultural cues are present and remaining neutral when they are absent. We propose CoCoA (Context-Conditional Cultural Alignment), a framework that learns this behavior through dual-context training on the same entity pairs under contexts with and without cultural cues. CoCoA combines a contrastive alignment objective with calibration and drift regularization, optimized through goal-aware gradient reconciliation. We evaluate CoCoA on CAMeL and Camellia, two entity-centric cultural bias benchmarks, across ten language settings and four LLMs. CoCoA reduces the Cultural Bias Score from 43 to 24 on average while maintaining near-neutral preferences at 50.2, with minimal impact on general performance across five standard benchmarks. These findings highlight that effective cultural alignment requires context-conditional modeling rather than uniform debiasing, and establish a new direction for mitigating entity-centric cultural bias in LLMs.

Problem

• Cultural Bias
• Context-Conditional
• Large Language Models

Innovation

• Context-Conditional
• Cultural Alignment
• Dual-Context Training
• Contrastive Alignment
• Gradient Reconciliation