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

• URL: http://arxiv.org/abs/2408.01346v1
• Date: Fri, 2 Aug 2024 15:46:36 GMT
• Title: Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks
• Authors: Anders Giovanni Møller, Luca Maria Aiello,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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