LLMs struggle to link figurative language and cultural knowledge via fine-tuning

A new research paper investigates how large language models (LLMs) handle figurative and cultural knowledge, exploring whether fine-tuning on specific cultural data improves their understanding of figurative language. The study used four models—ALLaM-7B, Fanar-1-9B, Qwen3-8B, and Llama-3.1-8B—and six Arabic datasets. Results showed that fine-tuning on poetry enhanced idiom comprehension, but cultural fine-tuning decreased proverb interpretation accuracy. The research suggests that while LLMs can adapt to figurative content, the relationship between culture and figurative language is complex and not easily captured through fine-tuning alone. AI

IMPACT Suggests limitations in current LLM fine-tuning methods for capturing nuanced cultural and figurative language understanding.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• ALLaM
• ArabicMMLU
• Fanar-1-9B
• Hugging Face
• Llama-3.1:8b
• Qwen3_8B

• paper
• model release

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