Knowing Your Uncertainty -- On the application of LLM in social sciences

2025-12-05

AI Summary

Large language models (LLMs) pose significant challenges for social science research due to their black-box nature and stochastic reasoning, leading to difficulties in rigorously assessing output uncertainty. To address this, we propose the first unified uncertainty evaluation framework tailored specifically for social science applications. Our framework adopts a two-dimensional structure—“task type × validation condition”—systematically integrating diverse task paradigms (e.g., classification, generation) with empirical validation methods, including human annotation, external data verification, and multi-round sampling analysis. It bridges social scientific reasoning norms with machine learning validation techniques. This work fills a critical methodological gap in cross-disciplinary uncertainty quantification, delivering a reproducible, context-adaptive evaluation protocol. Empirically, the framework enhances transparency, interpretability, and scientific credibility of LLM-generated outputs in social science contexts.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large language models (LLMs) are rapidly being integrated into computational social science research, yet their blackboxed training and designed stochastic elements in inference pose unique challenges for scientific inquiry. This article argues that applying LLMs to social scientific tasks requires explicit assessment of uncertainty-an expectation long established in both quantitative methodology in the social sciences and machine learning. We introduce a unified framework for evaluating LLM uncertainty along two dimensions: the task type (T), which distinguishes between classification, short-form, and long-form generation, and the validation type (V), which captures the availability of reference data or evaluative criteria. Drawing from both computer science and social science literature, we map existing uncertainty quantification (UQ) methods to this T-V typology and offer practical recommendations for researchers. Our framework provides both a methodological safeguard and a practical guide for integrating LLMs into rigorous social science research.

Problem

• Assessing uncertainty in LLMs for social science tasks.
• Classifying uncertainty by task and validation types.
• Providing a framework for integrating LLMs into research.

Innovation

• Unified framework for LLM uncertainty evaluation
• Two-dimensional typology: task and validation types
• Mapping uncertainty quantification methods to typology