  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.
