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Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging

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Aarne Talman, Hande Celikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann

Computation and Language · Submitted 10 Apr 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The results reveal the importance of uncertainty modeling, an often neglected aspect of neural language modeling, in NLU tasks.

how it is stated · asserted 1.00 read the record →

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arXiv ID2304.04726 (version 1)
Pith IDTH7UZXCQ, Pith's permanent record of this paper and its review
CitationAarne Talman, Hande Celikkanat, J\"org Tiedemann, Markus Heinonen, Sami Virpioja. "Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging." arXiv:2304.04726 (2023). https://pith.science/paper/TH7UZXCQ
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,576 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.09 · human participants 0.16 · controlled comparison 0.14 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.18 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. We apply the approach to standard tasks in natural language inference (NLI) and demonstrate the effectiveness of the method in terms of prediction accuracy and correlation with human annotation disagreements. We argue that the uncertainty representations in SWAG better reflect subjective interpretation and the natural variation that is also present in human language understanding. The results reveal the importance of uncertainty modeling, an often neglected aspect of neural language modeling, in NLU tasks.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. contribution 0.96 0.84 0.08 asserted 1.00
s1 We apply the approach to standard tasks in natural language inference (NLI) and demonstrate the effectiveness of the method in terms of prediction accuracy and correlation with human annotation disagreements. contribution 0.49 0.96 0.20 asserted 1.00
s2 We argue that the uncertainty representations in SWAG better reflect subjective interpretation and the natural variation that is also present in human language understanding. interpretation 1.00 0.30 0.32 asserted 0.86
s3 The results reveal the importance of uncertainty modeling, an often neglected aspect of neural language modeling, in NLU tasks. interpretation 0.91 0.85 0.70 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.98 · evidence from observation or experiment 0.86.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. We apply the approach to standard tasks in natural language inference (NLI) and demonstrate the effectiveness of the method in terms of prediction accuracy and correlation with human annotation disagreements. We argue that the uncertainty representations in SWAG better reflect subjective interpretation and the natural variation that is also present in human language understanding. The results reveal the importance of uncertainty modeling, an often neglected aspect of neural language modeling, in NLU tasks.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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