Ensemble-Based Uncertainty Quantification for Reliable Large Language Model Classification in Social Data Applications

Ensemble-Based Uncertainty Quantification for Reliable Large Language Model Classification in Social Data Applications

Assessing classification confidence is essential for effectively leveraging Large Language Models (LLMs) in automated data labeling, particularly within the sensitive contexts of Computational Social Science (CSS) tasks. In this study, we evaluate five uncertainty quantification (UQ) strategies acro...

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Bibliographic Details
Main Authors: David T. Farr, Lynnette Hui Xian Ng, Iain J. Cruickshank, Nico Manzonelli, Nicholas Clark, Kate Starbird, Nathaniel D. Bastian, Jevin West
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Data classification
large language models
uncertainty quantification
zero-shot classification
Online Access:https://ieeexplore.ieee.org/document/11069263/
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https://ieeexplore.ieee.org/document/11069263/

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