This paper presents two self-contained tutorials on stance detection in Twitter data using BERT fine-tuning and prompting large language models (LLMs). The first tutorial explains BERT architecture and tokenization, guiding users through training, tuning, and evaluating standard and domain-specific BERT models with HuggingFace transformers. The second focuses on constructing prompts and few-shot examples to elicit stances from ChatGPT and open-source FLAN-T5 without fine-tuning. Various prompting strategies are implemented and evaluated using confusion matrices and macro F1 scores. The tutorials provide code, visualizations, and insights revealing the strengths of few-shot ChatGPT and FLAN-T5 which outperform fine-tuned BERTs. By covering both model fine-tuning and prompting-based techniques in an accessible, hands-on manner, these tutorials enable learners to gain applied experience with cutting-edge methods for stance detection.

本文提出了两个自包含教程，介绍了使用BERT fine-tuning和使用大型语言模型（LLMs）进行推特数据中的立场检测的方法。第一个教程解释了BERT架构和标记化，引导用户进行训练、调优和评估使用HuggingFace transformers的标准和领域特定的BERT模型。第二个教程专注于构建提示和少样本示例，从ChatGPT和开源FLAN-T5中获取立场而无需进行fine-tuning。使用混淆矩阵和宏F1得分来实施和评估各种提示策略。这些教程提供了代码、可视化和洞察力，揭示了ChatGPT和FLAN-T5强于经过fine-tuning的BERT的优势。通过以易于理解和实用的方式涵盖模型fine-tuning和基于提示的技术，这些教程使学习者能够获得有关立场检测的前沿方法的应用经验。

使用预训练语言模型的立场检测教程：BERT微调与大型语言模型引导