Evaluation of Pretrained and Instruction-Based Pretrained Models for Emotion Detection in Arabic Social Media Text
Year: 2025
Type: conference-paper
Abstract: This study evaluates three approaches-instruction prompting of large language models (LLMs), instruction finetuning of LLMs, and transformer-based pretrained models on emotion detection in Arabic social media text.We compare pretrained transformer models like AraBERT, CaMelBERT, and XLM-RoBERTa with instruction prompting with advanced LLMs like GPT-4o, Gemini, Deepseek, and Fanar, and instruction fine-tuning approaches with LLMs like Llama 3.1, Mistral, and Phi.With a highly preprocessed dataset of 10,000 labeled Arabic tweets with overlapping emotional labels, our findings reveal that transformer-based pretrained models outperform instruction prompting and instruction fine-tuning approaches.Instruction prompts leverage general linguistic skills with maximum efficiency but fall short in detecting subtle emotional contexts.Instruction fine-tuning is more specific but trails behind pretrained transformer models.Our findings establish the need for optimized instruction-based approaches and underscore the important role played by domain-specific transformer architectures in accurate Arabic emotion detection.
Institution: Northwestern University in Qatar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7122745031