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This paper presents a comparative study of parameter efficient fine-tuning and prompt engineering techniques for hate speech classification in Roman Urdu.
The paper evaluates Large Language Models for hate speech detection in Roman Urdu, a low-resource language, demonstrating that Parameter-Efficient Fine-Tuning with LoRA significantly improves classification performance compared to zero-shot inference.