LLMs Require Explicit Uncertainty Assessment Framework
Bolun Zhang (submitted Dec. 5, 2025) argues that large language models' opaque training and stochastic inference require explicit uncertainty assessment in computational social science research. The paper introduces a unified T–V framework evaluating uncertainty by task type (classification, short-form, long-form) and validation type (availability of reference data), mapping existing uncertainty-quantification methods and offering practical researcher recommendations.
Key Points
- 1Introduce unified T–V framework classifying tasks: classification, short-form, and long-form generation.
- 2Highlight necessity of uncertainty quantification because LLMs' opaque training and stochastic inference increase result uncertainty.
- 3Recommend mapping UQ methods to task-validation types to enable reproducible, rigorous computational social science findings.
Scoring Rationale
Practical, novel UQ framework tailored to social-science LLMs; limited by single-author preprint and absent peer-reviewed validation.
Sources
Public references used for this report.
Practice with real Logistics & Shipping data
90 SQL & Python problems · 15 industry datasets
250 free problems · No credit card
See all Logistics & Shipping problems
