CultureLLM: Incorporating Cultural Differences into Large Language
  Models
Cheng Li Mengzhou Chen Jindong Wang Sunayana Sitaram Xing Xie
Large language models (LLMs) are reported to be partial to certain cultures
owing to the training data dominance from the English corpora. Since
multilingual cultural data are often expensive to collect, existing efforts
handle this by prompt engineering or culture-specific pre-training. However,
they might overlook the knowledge deficiency of low-resource culture and
require extensive computing resources. In this paper, we propose CultureLLM, a
cost-effective solution to incorporate cultural differences into LLMs.
CultureLLM adopts World Value Survey (WVS) as seed data and generates
semantically equivalent training data via the proposed semantic data
augmentation. Using only 50 seed samples from WVS with augmented data, we
fine-tune culture-specific LLMs and one unified model (CultureLLM-One) for 9
cultures covering rich and low-resource languages. Extensive experiments on 60
culture-related datasets demonstrate that CultureLLM significantly outperforms
various counterparts such as GPT-3.5 (by 8.1%) and Gemini Pro (by 9.5%) with
comparable performance to GPT-4 or even better. Our human study shows that the
generated samples are semantically equivalent to the original samples,
providing an effective solution for LLMs augmentation.