Large Language Models are expressive tools that enable complex tasks of text understanding within Computational Social Science. Their versatility, while beneficial, poses a barrier for establishing standardized best practices within the field. To bring clarity on the values of different strategies, we present an overview of the performance of modern LLM-based classification methods on a benchmark of 23 social knowledge tasks. Our results point to three best practices: select models with larger vocabulary and pre-training corpora; avoid simple zero-shot in favor of AI-enhanced prompting; fine-tune on task-specific data, and consider more complex forms instruction-tuning on multiple datasets only when only training data is more abundant.

本研究解决了计算社会科学领域中使用大语言模型（LLMs）的标准化最佳实践缺乏的问题。通过对23个社会知识任务的基准测试，发现三项最佳实践：选择具有更大词汇量和预训练语料库的模型；避免简单的零样本，而应优先使用AI增强提示；在任务特定数据上进行微调，并仅在训练数据丰富时考虑更复杂的指令微调形式。本研究为该领域提供了实用的指导。 

提示优化还是微调？在计算社会科学任务中使用大语言模型的最佳实践