Papers with interpretable evaluation

1 papers
CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis (2025.acl-long)

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Challenge: Large Language Models exhibit a specific cultural bias, neglecting values and differences of low-resource regions.
Approach: They propose a culturally-aware training paradigm that leverages multilingual data and fine-grained reward modeling to enhance cultural sensitivity and inclusivity.
Outcome: The proposed model achieves state-of-the-art in cultural alignment and general reasoning.

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