Sarcasm detection in multilingual and code-mixed environments remains a challenging task for natural language processing models due to structural variations, informal expressions, and low-resource linguistic availability. This study compares four large language models, Llama 3.1, Mistral, Gemma 3, and Phi-4, with a fine-tuned DistilBERT model for sarcasm detection in code-mixed Hinglish text. The results indicate that the smaller, sequentially fine-tuned DistilBERT model achieved the highest overall accuracy of 84%, outperforming all of the LLMs in zero and few-shot set ups, using minimal LLM generated code-mixed data used for fine-tuning. These findings indicate that domain-adaptive fine-tuning of smaller transformer based models may significantly improve sarcasm detection over general LLM inference, in low-resource and data scarce settings.

翻译：在多语言和代码混合环境中进行讽刺检测对自然语言处理模型而言仍是一项具有挑战性的任务，这源于结构变异、非正式表达以及低资源语言可用性。本研究比较了四种大型语言模型（Llama 3.1、Mistral、Gemma 3和Phi-4）与一个经过微调的DistilBERT模型在代码混合印地英语文本中的讽刺检测性能。结果表明，经过序列微调的小型DistilBERT模型取得了84%的最高总体准确率，在零样本和少样本设置下均优于所有大型语言模型，且微调过程仅使用了极少量由大型语言模型生成的代码混合数据。这些发现表明，在低资源和数据稀缺场景下，对基于Transformer的小型模型进行领域自适应微调，可能比通用大型语言模型的推理在讽刺检测任务上带来显著提升。