Stance detection, a key task in natural language processing, determines an author's viewpoint based on textual analysis. This study evaluates the evolution of stance detection methods, transitioning from early machine learning approaches to the groundbreaking BERT model, and eventually to modern Large Language Models (LLMs) such as ChatGPT, LLaMa-2, and Mistral-7B. While ChatGPT's closed-source nature and associated costs present challenges, the open-source models like LLaMa-2 and Mistral-7B offers an encouraging alternative. Initially, our research focused on fine-tuning ChatGPT, LLaMa-2, and Mistral-7B using several publicly available datasets. Subsequently, to provide a comprehensive comparison, we assess the performance of these models in zero-shot and few-shot learning scenarios. The results underscore the exceptional ability of LLMs in accurately detecting stance, with all tested models surpassing existing benchmarks. Notably, LLaMa-2 and Mistral-7B demonstrate remarkable efficiency and potential for stance detection, despite their smaller sizes compared to ChatGPT. This study emphasizes the potential of LLMs in stance detection and calls for more extensive research in this field.

中文翻译：

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使用微调的大型语言模型进行社交媒体上的立场检测

立场检测是自然语言处理中的一项关键任务，它根据文本分析确定作者的观点。这项研究评估了姿态检测方法的演变，从早期的机器学习方法过渡到突破性的 BERT 模型，并最终过渡到 ChatGPT、LLaMa-2 和 Mistral-7B 等现代大型语言模型 (LLM)。虽然 ChatGPT 的闭源性质和相关成本带来了挑战，但 LLaMa-2 和 Mistral-7B 等开源模型提供了令人鼓舞的替代方案。最初，我们的研究重点是使用几个公开可用的数据集对 ChatGPT、LLaMa-2 和 Mistral-7B 进行微调。随后，为了提供全面的比较，我们评估了这些模型在零样本和少样本学习场景中的性能。结果强调了法学硕士在准确检测立场方面的卓越能力，所有测试的模型都超越了现有基准。值得注意的是，LLaMa-2 和 Mistral-7B 尽管尺寸比 ChatGPT 更小，但在姿态检测方面表现出了卓越的效率和潜力。这项研究强调了法学硕士在姿态检测方面的潜力，并呼吁在这一领域进行更广泛的研究。