New framework uses Hill numbers to quantify LLM uncertainty in social sciences

A new research paper proposes a framework for evaluating uncertainty in Large Language Models (LLMs) when applied to social science research. The paper argues that explicit assessment of uncertainty is crucial for scientific inquiry, a principle long established in both social sciences and machine learning. The proposed framework utilizes Hill numbers, a family of diversity measures, to unify existing uncertainty quantification metrics on a common scale, accommodating various notions of semantic similarity and output distribution sensitivities. This approach is demonstrated through four empirical applications in social sciences. AI

IMPACT Provides a standardized method for assessing LLM reliability in social science research, potentially improving the rigor of AI applications in this field.

RANK_REASON Academic paper detailing a new methodology for LLM uncertainty quantification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Bolun Zhang
• Large Language Models
• uncertainty quantification

• paper
• other

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