Figurative and Cultural Knowledge in LLMs: Investigating Cross-Domain Transfer through Fine-Tuning
Fine-tuning poetry data improves Arabic idiom understanding by 2.33%; cultural fine-tuning shows no significant effect.
Key Findings
Methodology
The study fine-tuned four LLMs (ALLaM-7B, Fanar-1-9B, Qwen3-8B, Llama-3.1-8B) on six Arabic datasets covering cultural commonsense, proverbs, and poetry to analyze cross-domain transfer between cultural knowledge and figurative language understanding.
Key Results
- Fine-tuning poetry data improved idiom comprehension by 2.33% (p<0.05), indicating sensitivity to non-literal meaning that transfers across figurative types.
- Cultural fine-tuning reduced proverb interpretation accuracy, with Arabic-centric models showing knowledge regression due to prior saturation.
- Multilingual models demonstrated greater adaptability and room for improvement compared to Arabic-centric models.
Significance
The study challenges the assumption that cultural knowledge and figurative language understanding can straightforwardly support each other through fine-tuning. It provides insights for developing culturally sensitive language models.
Technical Contribution
Introduced a controlled framework to study bidirectional transfer between cultural knowledge and figurative language understanding, identifying poetry fine-tuning as the only reliable source of positive figurative transfer.
Novelty
First systematic exploration of the interplay between cultural knowledge and figurative language understanding, highlighting poetry's unique contribution to idiom comprehension and the phenomenon of knowledge regression under cultural fine-tuning.
Limitations
- Cultural fine-tuning did not enhance figurative language understanding and caused performance regressions.
- The study is limited to Arabic datasets, potentially restricting generalizability to other languages.
- The mechanisms behind knowledge regression remain unexplored.
Future Work
Future research could explore advanced fine-tuning methods, multimodal data integration, or novel architectures to better capture the interplay between culture and figurative language.
AI Executive Summary
Figurative language, such as proverbs and poetry, is deeply rooted in culture, requiring both linguistic competence and cultural immersion for fluent interpretation. This study investigates whether fine-tuning large language models (LLMs) on cultural data improves figurative language understanding, and vice versa.
Using four LLMs (ALLaM-7B, Fanar-1-9B, Qwen3-8B, Llama-3.1-8B) and six Arabic datasets spanning cultural commonsense, proverbs, and poetry, the study found that fine-tuning poetry data significantly enhanced idiom comprehension (+2.33%, p<0.05). However, cultural fine-tuning reduced proverb interpretation accuracy, particularly in Arabic-centric models, which showed signs of knowledge saturation.
The findings reveal that the relationship between cultural knowledge and figurative language understanding is complex and cannot be captured through simple fine-tuning. Future research should explore advanced methods and architectures to better understand and leverage this interplay.
Deep Analysis
Background
Figurative language encodes cultural values and norms, requiring more than linguistic skills for interpretation. Prior work has largely studied cultural knowledge and figurative language separately, leaving their interaction unexplored.
Core Problem
The study seeks to answer whether cultural fine-tuning improves figurative language understanding, whether figurative data enhances cultural knowledge, and whether transfer occurs across figurative forms.
Innovation
Developed a systematic framework to study cross-domain transfer between cultural knowledge and figurative language. Found poetry fine-tuning uniquely enhances idiom comprehension and identified knowledge regression under cultural fine-tuning.
Methodology
- �� Fine-tuned four LLMs (ALLaM-7B, Fanar-1-9B, Qwen3-8B, Llama-3.1-8B)
- �� Used six Arabic datasets covering cultural commonsense, proverbs, and poetry
- �� Designed three experiments: cultural fine-tuning's impact on figurative understanding, figurative fine-tuning's impact on cultural understanding, and a control for Arabic adaptation
Experiments
Experiments used publicly available datasets like Jawaher and FannOrFlop. Fine-tuning employed LoRA, and evaluations were conducted using lm-eval under multiple random seeds to ensure robustness.
Results
Poetry fine-tuning improved idiom comprehension (+2.33%, p<0.05). Cultural fine-tuning reduced proverb interpretation accuracy, with Arabic-centric models showing knowledge saturation. Multilingual models exhibited greater adaptability.
Applications
Direct applications include improving cultural sensitivity in language models for cross-cultural communication and education. Future work could extend to other languages and cultures.
Limitations & Outlook
Cross-domain transfer effects were limited, with model responses highly dependent on initial knowledge levels. The study focused solely on Arabic datasets, limiting broader applicability.
Plain Language Accessible to non-experts
Imagine an AI as a chef. Cultural knowledge is the ingredients, and figurative language is the special spices. Fine-tuning poetry data teaches the chef to use new spices, making dishes (idioms) more flavorful. But teaching the chef about cultural ingredients might confuse them about how to use spices correctly (proverb understanding worsens).
ELI14 Explained like you're 14
Think of it like a game where the AI solves puzzles about language. Some puzzles are about poems, others about proverbs. The study found that teaching the AI more poems helps it solve idiom puzzles better! But if you teach it cultural facts, it might actually get worse at solving proverb puzzles. Weird, right? Maybe it already knows too much about culture and gets overloaded!
Glossary
Fine-tuning
A method to optimize model performance using additional training data.
Used to enhance LLMs' understanding of cultural and figurative language.
Figurative Language
Non-literal language forms like proverbs and poetry.
A core task in the study to evaluate model comprehension.
Cultural Knowledge
Knowledge about social norms, history, and practices.
Tested through cultural commonsense reasoning benchmarks.
LoRA
A parameter-efficient fine-tuning technique using low-rank matrices.
Applied to fine-tune the four LLMs in the study.
Jawaher
An Arabic proverb interpretation dataset.
Used to evaluate models' proverb comprehension.
Open Questions Unanswered questions from this research
- 1 How can fine-tuning methods better capture the interplay between culture and figurative language?
- 2 Why does cultural fine-tuning lead to knowledge regression in Arabic-centric models?
- 3 Can these findings generalize to other languages and cultures?
Applications
Immediate Applications
Cross-cultural communication
Enhance language models' cultural sensitivity for better understanding of diverse expressions.
Educational tools
Support language learning and cultural education by improving figurative language comprehension.
Long-term Vision
Globalized AI tools
Develop intelligent assistants capable of understanding multiple cultures and languages for global communication.
Abstract
Figurative language is deeply culturally embedded; fluent use requires not just linguistic competence but cultural immersion. We ask whether LLMs can learn this link: does fine-tuning on cultural data improve figurative language understanding, and vice versa? We conduct a systematic study across four models (ALLaM-7B, Fanar-1-9B, Qwen3-8B, Llama-3.1-8B) and six Arabic datasets spanning cultural commonsense, proverbs, and poetry across diverse dialects and regions. Fine-tuning on poetry improves idiom comprehension (+2.33%, p<0.05), a gain our ArabicMMLU control does not reproduce, indicating that it stems from figurative content rather than Arabic language adaptation and pointing to a sensitivity to non-literal meaning that transfers across figurative types. Cultural fine-tuning, by contrast, lowers proverb-interpretation accuracy in both Arabic-centric models. Transfer between the two domains is otherwise indistinguishable from noise, with Arabic models frequently regressing after fine-tuning, suggesting prior saturation of relevant knowledge, while multilingual models show greater adaptation headroom. Error analysis further reveals that fine-tuning reinforces experiential cultural knowledge while destabilizing historically grounded factual knowledge. Our findings suggest that the relationship between culture and figurative language, though conceptually natural, is not straightforwardly captured through fine-tuning alone.