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About this paper
Lodewijk Gelauff, Mohak Goyal, Bhargav Dindukurthi, Ashish Goel, Alice Siu
A study from Stanford University's Center on Democracy, Development and the Rule of Law introduces a scalable method using GPT-4 to estimate contribution quality in online deliberation, based on four criteria. GPT-4's assessments proved more accurate than individual human annotators and competitive with pairs of humans, while reducing costs (under $4 per event) and processing time (under 2-second delays) compared to traditional human annotation, and demonstrated that participation-boosting nudges did not degrade contribution quality.
Key insights
- Large Language Models (LLMs) like GPT-4 can effectively and efficiently assess subjective deliberative quality, often outperforming individual human annotators and providing clearer justifications for their ratings.
- Automating quality assessment with LLMs offers significant improvements in scalability and cost-effectiveness (approximately four times cheaper and significantly faster) compared to traditional human content analysis.
- Platform interventions like 'Speak' nudges, designed to increase participation, can successfully boost engagement without compromising the quality of contributions in online deliberations.
Summary by alphaXiv.
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