← Back to all news
arXiv Computation and Language

CoCoA: Context-Conditional Cultural Alignment for Large Language Models

CoCoA (Context-Conditional Cultural Alignment) is a framework designed to mitigate cultural bias in large language models by learning context-conditional behavior. It trains on entity pairs under both culturally cued and neutral contexts, using a contrastive alignment objective combined with calibration, drift regularization, and goal-aware gradient reconciliation. Evaluations on CAMeL and Camellia across ten languages and four LLMs show that CoCoA reduces the Cultural Bias Score from 43 to 24 on average while keeping near-neutral preferences at 50.2, with minimal impact on general performance.

Related stories

arXiv Computation and Language
2d ago

Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages

Camellia is a new benchmark that tests cultural bias in large language models (LLMs) across nine Asian languages and six Asian cultures. It contains 19,530 manually annotated entities linked to Asian or Western cultures and 2,173 masked social‑media contexts for these entities. Using Camellia, the authors evaluate four multilingual LLMs on cultural context adaptation, sentiment association, and entity extractive QA, finding that models struggle with cultural adaptation, exhibit differing biases across regions and families, and have difficulty understanding context in some Asian languages.

By Tarek Naous, Anagha Savit, Carlos Rafael Catalan, Geyang Guo, Jaehyeok Lee, Kyungdon Lee, Lheane Marie Dizon, Mengyu Ye, Neel Kothari, Sahajpreet Singh, Sarah Masud, Tanish Patwa, Trung Thanh Tran, Zohaib Khan, Alan Ritter, Tanmoy Chakraborty, Yuki Arase, Keisuke Sakaguchi, JinYeong Bak, Wei Xu
llmsbenchmarks
More like this →
arXiv AI
Aug 21

DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values

arXiv:2509. 08022v3 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with diverse human values is essential for safe and effective deployment, yet existing benchmarks often overlook cultural and demographic variation.

By Yao Liang, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yuwei Wang, Dongqi Liang, Yi Zeng
llmsreinforcement-learningfine-tuningbenchmarkssafety
More like this →
Hugging Face Trending Papers
Jun 3

BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization

Mitigating social bias in Large Language Models (LLMs) presents a distinct alignment challenge: unlike verifiable tasks, bias lacks a single ground truth, creating a high-variance, subjective reward landscape. Previous preference-based fine-tuning methods have major trade-offs: Direct Preference Optimization (DPO) is limited by the lack of exploration inherent in offline training, while Proximal Policy Optimization (PPO) can lead to training instability due to potentially unreliable critic estimates.

llmsreinforcement-learningfine-tuningbenchmarkssafety
More like this →
arXiv AI
Aug 25

An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift

The paper investigates how preference tuning—optimizing language models with explicit preference signals—behaves when applied to new domains. It systematically compares five alignment objectives and several adaptation strategies, such as target‑domain supervised fine‑tuning and pseudo‑labeling, across summarization, question‑answering helpfulness, and safety tasks. Results show that while pseudo‑labeling reduces domain‑shift degradation, it also causes mode collapse, highlighting a trade‑off between generalization and diversity.

By Constantinos Karouzos, Xingwei Tan, Nikolaos Aletras
llmsnlpfine-tuningsafety
More like this →