Figurative and Cultural Knowledge in LLMs: Investigating Cross-Domain Transfer through Fine-Tuning

Summary

A systematic study investigated whether fine-tuning Large Language Models (LLMs) on cultural data improves figurative language understanding and vice versa. Researchers used four models, including ALLaM-7B and Fanar-1-9B, and six Arabic datasets covering cultural commonsense, proverbs, and poetry across various dialects. They found that fine-tuning on poetry improved idiom comprehension by 2.33%, suggesting a transfer of non-literal meaning understanding across figurative types. Why it matters: This research highlights the complex challenges of integrating nuanced cultural and figurative language understanding into LLMs, particularly for Arabic, suggesting that fine-tuning alone may not fully capture these intricate relationships.