Ensemble-Based Uncertainty Quantification for Reliable Large Language Model Classification in Social Data Applications Article Swipe
David F. Farr
,
Lynnette Hui Xian Ng
,
Iain J. Cruickshank
,
Nico Manzonelli
,
Nicholas A. Clark
,
Kate Starbird
,
Nathaniel D. Bastian
,
Jevin D. West
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3585414
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3585414
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 across three CSS classification problems: stance detection, ideology identification, and frame detection. We benchmark these strategies using three different LLMs. To enhance human-in-the-loop classification performance, we introduce an ensemble-based UQ aggregation method, C_ensemble, and propose a novel evaluation metric, Misclassified Recall, designed to better assess model uncertainty on mislabeled or ambiguous data points. Our results show that C_ensemble outperforms existing UQ techniques in six out of nine model-dataset combinations, achieving an average AUC improvement of 8.7%. These findings highlight the potential of UQ-driven methods to significantly improve the reliability and efficiency of human-in-the-loop data annotation pipelines.
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https://doi.org/10.1109/access.2025.3585414Digital Object Identifier
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Ensemble-Based Uncertainty Quantification for Reliable Large Language Model Classification in Social Data ApplicationsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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David F. Farr, Lynnette Hui Xian Ng, Iain J. Cruickshank, Nico Manzonelli, Nicholas A. Clark, Kate Starbird, Nathaniel D. Bastian, Jevin D. WestList of authors in order
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https://doi.org/10.1109/access.2025.3585414Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3585414Direct OA link when available
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Computer science, Ensemble learning, Artificial intelligence, Data mining, Machine learningTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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47Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4396809921, https://openalex.org/W4384200891, https://openalex.org/W4410376226, https://openalex.org/W6862111818, https://openalex.org/W4389636360, https://openalex.org/W4392240262, https://openalex.org/W4402827393, https://openalex.org/W4389518686, https://openalex.org/W4394780772, https://openalex.org/W4408812939, https://openalex.org/W4396218239, https://openalex.org/W6778883912, https://openalex.org/W6839548382, https://openalex.org/W3099474967, https://openalex.org/W6801617135, https://openalex.org/W4396831993, https://openalex.org/W3047027807, https://openalex.org/W4404782982, https://openalex.org/W6851151028, https://openalex.org/W6875092577, https://openalex.org/W6853906918, https://openalex.org/W6874851699, https://openalex.org/W4401043565, https://openalex.org/W6850423384, https://openalex.org/W6730042731, https://openalex.org/W6857598455, https://openalex.org/W4294597242, https://openalex.org/W4401630357, https://openalex.org/W4285301760, https://openalex.org/W6753084064, https://openalex.org/W2460159515, https://openalex.org/W2161920755, https://openalex.org/W2741212913, https://openalex.org/W2566963797, https://openalex.org/W560287614, https://openalex.org/W2130237711, https://openalex.org/W2001442493, https://openalex.org/W6874219447, https://openalex.org/W4400055904, https://openalex.org/W4400004771, https://openalex.org/W2986889260, https://openalex.org/W4404782678, https://openalex.org/W4380301730, https://openalex.org/W3214119601, https://openalex.org/W2884295076, https://openalex.org/W4295872590, https://openalex.org/W4395484085 |
| referenced_works_count | 47 |
| abstract_inverted_index.a | 72 |
| abstract_inverted_index.In | 27 |
| abstract_inverted_index.To | 57 |
| abstract_inverted_index.UQ | 66, 97 |
| abstract_inverted_index.We | 49 |
| abstract_inverted_index.an | 64, 107 |
| abstract_inverted_index.in | 12, 99 |
| abstract_inverted_index.is | 3 |
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| abstract_inverted_index.on | 84 |
| abstract_inverted_index.or | 86 |
| abstract_inverted_index.to | 79, 121 |
| abstract_inverted_index.we | 30, 62 |
| abstract_inverted_index.AUC | 109 |
| abstract_inverted_index.CSS | 39 |
| abstract_inverted_index.Our | 90 |
| abstract_inverted_index.and | 46, 70, 126 |
| abstract_inverted_index.for | 5 |
| abstract_inverted_index.out | 101 |
| abstract_inverted_index.six | 100 |
| abstract_inverted_index.the | 18, 116, 124 |
| abstract_inverted_index.(UQ) | 35 |
| abstract_inverted_index.data | 14, 88, 130 |
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| abstract_inverted_index.nine | 103 |
| abstract_inverted_index.show | 92 |
| abstract_inverted_index.that | 93 |
| abstract_inverted_index.this | 28 |
| abstract_inverted_index.(CSS) | 25 |
| abstract_inverted_index.8.7%. | 112 |
| abstract_inverted_index.LLMs. | 56 |
| abstract_inverted_index.Large | 8 |
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| abstract_inverted_index.frame | 47 |
| abstract_inverted_index.model | 82 |
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| abstract_inverted_index.these | 51 |
| abstract_inverted_index.three | 38, 54 |
| abstract_inverted_index.using | 53 |
| abstract_inverted_index.(LLMs) | 11 |
| abstract_inverted_index.Models | 10 |
| abstract_inverted_index.Social | 23 |
| abstract_inverted_index.across | 37 |
| abstract_inverted_index.assess | 81 |
| abstract_inverted_index.better | 80 |
| abstract_inverted_index.stance | 42 |
| abstract_inverted_index.study, | 29 |
| abstract_inverted_index.tasks. | 26 |
| abstract_inverted_index.within | 17 |
| abstract_inverted_index.Recall, | 77 |
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| abstract_inverted_index.enhance | 58 |
| abstract_inverted_index.improve | 123 |
| abstract_inverted_index.method, | 68 |
| abstract_inverted_index.methods | 120 |
| abstract_inverted_index.metric, | 75 |
| abstract_inverted_index.points. | 89 |
| abstract_inverted_index.propose | 71 |
| abstract_inverted_index.results | 91 |
| abstract_inverted_index.Language | 9 |
| abstract_inverted_index.contexts | 20 |
| abstract_inverted_index.designed | 78 |
| abstract_inverted_index.evaluate | 31 |
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| abstract_inverted_index.findings | 114 |
| abstract_inverted_index.ideology | 44 |
| abstract_inverted_index.Assessing | 0 |
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| abstract_inverted_index.achieving | 106 |
| abstract_inverted_index.ambiguous | 87 |
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| abstract_inverted_index.different | 55 |
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| abstract_inverted_index.introduce | 63 |
| abstract_inverted_index.labeling, | 15 |
| abstract_inverted_index.potential | 117 |
| abstract_inverted_index.problems: | 41 |
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| abstract_inverted_index.confidence | 2 |
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| abstract_inverted_index.detection. | 48 |
| abstract_inverted_index.efficiency | 127 |
| abstract_inverted_index.evaluation | 74 |
| abstract_inverted_index.leveraging | 7 |
| abstract_inverted_index.mislabeled | 85 |
| abstract_inverted_index.pipelines. | 132 |
| abstract_inverted_index.strategies | 36, 52 |
| abstract_inverted_index.techniques | 98 |
| abstract_inverted_index.C_ensemble, | 69 |
| abstract_inverted_index.aggregation | 67 |
| abstract_inverted_index.effectively | 6 |
| abstract_inverted_index.improvement | 110 |
| abstract_inverted_index.outperforms | 95 |
| abstract_inverted_index.reliability | 125 |
| abstract_inverted_index.uncertainty | 33, 83 |
| abstract_inverted_index.particularly | 16 |
| abstract_inverted_index.performance, | 61 |
| abstract_inverted_index.Computational | 22 |
| abstract_inverted_index.Misclassified | 76 |
| abstract_inverted_index.combinations, | 105 |
| abstract_inverted_index.model-dataset | 104 |
| abstract_inverted_index.significantly | 122 |
| abstract_inverted_index.classification | 1, 40, 60 |
| abstract_inverted_index.ensemble-based | 65 |
| abstract_inverted_index.quantification | 34 |
| abstract_inverted_index.identification, | 45 |
| abstract_inverted_index.human-in-the-loop | 59, 129 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.11681659 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |