Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis

• URL: http://arxiv.org/abs/2605.10415v2
• Date: Wed, 13 May 2026 13:41:26 GMT
• Title: Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis
• Abstract summary: We advocate uncertainty-aware subjectivity analysis, where models are expected to make predictions while expressing uncertainty that reflects human disagreement.<n>To operationalize this perspective, we propose a two-phase Disagreement Perception and Uncertainty Alignment framework.<n> Experiments on three subjectivity analysis tasks show that DPUA preserves task performance while better aligning model uncertainty with human disagreement.
• Score: 57.78407973423517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models for subjectivity analysis are typically trained with aggregated labels, which compress variations in human judgment into a single supervision signal. This paradigm overlooks the intrinsic uncertainty of low-agreement samples and often induces overconfident predictions, undermining reliability and generalization in complex subjective settings. In this work, we advocate uncertainty-aware subjectivity analysis, where models are expected to make predictions while expressing uncertainty that reflects human disagreement. To operationalize this perspective, we propose a two-phase Disagreement Perception and Uncertainty Alignment (DPUA) framework. Specifically, DPUA jointly models label prediction, rationale generation, and uncertainty expression under an uncertainty-aware setting. In the disagreement perception phase, adaptive decoupled learning enhances the model's sensitivity to disagreement-related cues while preserving task performance. In the uncertainty alignment phase, GRPO-based reward optimization further improves uncertainty-aware reasoning and aligns the model's confidence expression with the human disagreement distribution. Experiments on three subjectivity analysis tasks show that DPUA preserves task performance while better aligning model uncertainty with human disagreement, mitigating overconfidence on boundary samples, and improving out-of-distribution generalization.

Related papers

• It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces [23.472868138533926]
  Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions.<n>This paper addresses the dual nature of uncertainty, focusing on their joint integration in high-dimensional regression tasks.<n>We propose a novel approach that approximates the resulting joint uncertainty using a low-rank plus diagonal covariance structure.
  arXiv  Detail & Related papers  (2026-08-25T13:08:17Z)
• Sensitivity Uncertainty Alignment in Large Language Models [0.0]
  We propose a framework for analyzing failures of large language models under adversarial and ambiguous inputs.<n>We define a scalar score, SUA_theta(x), capturing the difference between distributional sensitivity and predictive entropy.<n>We introduce SUA-TR, a training method combining consistency regularization and entropy alignment, along with an abstention rule for safer inference.
  arXiv  Detail & Related papers  (2026-04-21T17:53:12Z)
• Rubric-Guided Fine-tuning of SpeechLLMs for Multi-Aspect, Multi-Rater L2 Reading-Speech Assessment [8.300738063140129]
  We introduce a rubric-guided reasoning framework that explicitly encodes multi-aspect human assessment criteria.<n>We develop an uncertainty-calibrated regression approach supported by conformal calibration for interpretable confidence intervals.<n>Our results demonstrate that rubric-guided, uncertainty-calibrated reasoning offers a principled path toward trustworthy and explainable SpeechLLM-based speech assessment.
  arXiv  Detail & Related papers  (2026-02-28T11:08:24Z)
• Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP [25.097081181685613]
  Annotator disagreement is widespread in NLP, particularly for subjective and ambiguous tasks such as toxicity detection and stance analysis.<n>We first present a domain-agnostic taxonomy of the sources of disagreement spanning data, task, and annotator factors.<n>We then synthesize modeling approaches using a common framework defined by prediction targets and pooling structure.
  arXiv  Detail & Related papers  (2026-01-14T01:26:29Z)
• Benchmarking Corruption Robustness of LVLMs: A Discriminative Benchmark and Robustness Alignment Metric [49.393713730706445]
  We introduce Bench-C, a benchmark emphasizing discriminative samples for assessing corruption robustness.<n>We propose the Robustness Alignment Score (RAS), a unified metric that measures degradation in logit-level prediction structure.
  arXiv  Detail & Related papers  (2025-11-24T12:07:56Z)
• Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation [68.106428321492]
  Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding.<n>LLMs hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans.<n>We present Combined Uncertainty estimation for Reliable Embodied planning (CURE), which decomposes the uncertainty into epistemic and intrinsic uncertainty, each estimated separately.
  arXiv  Detail & Related papers  (2025-10-09T10:26:58Z)
• Uncertainty-Supervised Interpretable and Robust Evidential Segmentation [16.64288013782432]
  Uncertainty estimation has been widely studied in medical image segmentation as a tool to provide reliability.<n>Previous methods generally lack effective supervision in uncertainty estimation.<n>We propose a self-supervised approach to guide the learning of uncertainty.
  arXiv  Detail & Related papers  (2025-09-21T14:31:47Z)
• SConU: Selective Conformal Uncertainty in Large Language Models [59.25881667640868]
  We propose a novel approach termed Selective Conformal Uncertainty (SConU)<n>We develop two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level.<n>Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions.
  arXiv  Detail & Related papers  (2025-04-19T03:01:45Z)
• Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching [61.73532883992135]
  We propose a new uncertainty-aware stereo matching framework.<n>We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty.
  arXiv  Detail & Related papers  (2024-12-24T23:28:20Z)
• Toward Reliable Human Pose Forecasting with Uncertainty [51.628234388046195]
  We develop an open-source library for human pose forecasting, including multiple models, supporting several datasets. We devise two types of uncertainty in the problem to increase performance and convey better trust.
  arXiv  Detail & Related papers  (2023-04-13T17:56:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.