Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways

• URL: http://arxiv.org/abs/2406.11980v1
• Date: Mon, 17 Jun 2024 18:01:43 GMT
• Title: Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways
• Authors: Shubham Atreja, Joshua Ashkinaze, Lingyao Li, Julia Mendelsohn, Libby Hemphill,
• Abstract summary: We test how prompt design impacts the compliance and accuracy of social science annotations. Our results show that LLM compliance and accuracy are highly prompt-dependent. This work serves as both a warning and practical guide for researchers and practitioners.
• Score: 3.779027297957693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Manually annotating data for computational social science tasks can be costly, time-consuming, and emotionally draining. While recent work suggests that LLMs can perform such annotation tasks in zero-shot settings, little is known about how prompt design impacts LLMs' compliance and accuracy. We conduct a large-scale multi-prompt experiment to test how model selection (ChatGPT, PaLM2, and Falcon7b) and prompt design features (definition inclusion, output type, explanation, and prompt length) impact the compliance and accuracy of LLM-generated annotations on four CSS tasks (toxicity, sentiment, rumor stance, and news frames). Our results show that LLM compliance and accuracy are highly prompt-dependent. For instance, prompting for numerical scores instead of labels reduces all LLMs' compliance and accuracy. The overall best prompting setup is task-dependent, and minor prompt changes can cause large changes in the distribution of generated labels. By showing that prompt design significantly impacts the quality and distribution of LLM-generated annotations, this work serves as both a warning and practical guide for researchers and practitioners.

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