Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks

Autor:	Møller, Anders Giovanni, Aiello, Luca Maria
Rok vydání:	2024
Předmět:	Computer Science - Computers and Society Computer Science - Computation and Language Physics - Physics and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.01346 Zobrazit plný text záznamu View this record from Arxiv