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.
@article{arxiv.2408.01346,
title = {Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks},
author = {Anders Giovanni Møller and Luca Maria Aiello},
journal= {arXiv preprint arXiv:2408.01346},
year = {2024}
}