arXiv cs.AI论文
低资源语言仇恨言论检测中LLM的高效适配：罗马乌尔都语的比较研究
该论文针对低资源语言（如罗马乌尔都语）的仇恨言论检测，评估了多种大型语言模型（LLM）在零样本和参数高效微调（PEFT）下的性能。实验基于包含7.2万条标注评论的PURUTT数据集，结果显示零样本模型F1仅为0.56，而使用LoRA微调少量参数后，F1提升至0.93以上，表明PEFT在低资源语言处理中兼具高效性和高性能。
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Abstract:It is challenging to detect hate speech in Low Resource Languages (LRLs) because of the absence of annotated data, the informality of its language structure, and the lack of standardized grammar. A good example of such a challenge is Roman Urdu which is broadly used by South Asians on social media and has a high variation while lacking contextually consistent spellings. The objective of this paper is to conduct a comprehensive assessment of Large Language Models (LLMs) for Hate Speech Detection (HSD) in Roman Urdu script and fine-tune these models using the Parameter-Efficient Fine-Tuning (PEFT) method called Low-Rank Adaptation (LoRA). To evaluate zero-shot inference, we benchmarked it against PEFT on different transformer models, including Mistral, LLaMA, Falcon, and multilingual BERT. Experiments are conducted on the PURUTT (Parallel Urdu and Roman Urdu Corpus for Toxic Comments and Transliteration) dataset with over 72,000 annotated comments. The results suggest that zero shot models perform moderately (F1 = 0.56), but updating a small fraction of the model trainable parameters improves the classification performance significantly (F1 > 0.93). Our results have shown that PEFT delivers outstanding performance alongside excellent computational efficiency, making it highly suitable for low-resource language processing tasks.