Aug 2026· Frontiers in Artificial Intelligence· 0 citations· 53 references
TL;DR
An adaptive threshold selection policy that chooses thresholds on validation data using pseudo-label precision and sample count is introduced and is combined with confidence-aware verifier training to support confidence-based selection of pseudo-labeled subsets.
Abstract
This paper studies how to improve the reasoning ability of large language models (LLMs) with minimal supervision. Recent gains in LLM reasoning largely come from learning intermediate reasoning traces, and many methods reduce supervision cost by using traces whose final answers are correct. In realistic settings, however, obtaining even those answer labels can be costly, motivating methods that extend reasoning from a very small labeled set with abundant unlabeled questions. Verifier-based semi-supervised learning is a promising approach: a verifier trained on a small labeled set can score reasoning traces on unlabeled questions and identify candidates for pseudo-labeling. However, even with a verifier, it remains unclear how pseudo-labeled samples should be selected to support downstream training. In particular, pseudo-label selection must balance quality and quantity. To address this, we introduce an adaptive threshold selection policy that chooses thresholds on validation data using pseudo-label precision and sample count. We further combine this policy with confidence-aware verifier training to support confidence-based selection. Experiments on verifiable math reasoning benchmarks show that, under our training setup, this combination improves downstream reasoning accuracy over the tested baselines and selects pseudo-labeled subsets with a more favorable reliability–coverage trade-off. These results suggest a practical design direction for verifier-guided pseudo-label selection in answer-verifiable, minimal-label reasoning settings.
A critical challenge in deploying online HD map construction systems to real-world scenarios is the scarcity of labeled training data, which limits model generalization in diverse environments. To address this limitation, we propose a teacher-student semi-supervised learning (SSL) framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement. Our approach first trains a teacher model on limited labeled data, then leverages Beta-distribution-based confidence maps to assess the reliability of predicted map elements across temporal observations. Unlike conventional filtering methods that discard entire elements, we introduce a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments. The refined map elements serve as map priors that improve the teacher model's prediction accuracy on unlabeled data in a second pass. These enhanced predictions become pseudo-labels for training a student model from scratch, followed by fine-tuning on the original labeled data. Experimental results on the nuScenes dataset demonstrate that our teacher-student framework with refined pseudo-labels improves performance by +6.1 mAP under a low-label regime compared to training on labeled data alone, offering a practical solution to the labeled data scarcity problem in online HD map construction.
Chikao Tsuchiya, Dhaval Bhanderi, David Ilstrup et al.· 0 citations
Testing an LLM on two e-commerce data quality tasks, entity matching and brand mislabeling, against rule based baselines and human verified ground truth, under both zero-shot and few-shot prompting suggests that the value of using an LLM over traditional methods depends heavily on the task.
Large language models (LLMs) may generate fluent but incorrect answers, making uncertainty quantification important for reliable question answering. However, heuristic uncertainty scores cannot perfectly distinguish correct predictions from incorrect ones, and directly applying a fixed uncertainty threshold provides no statistical control over the error rate among accepted answers. To address this limitation, we propose A-CRC-QA, a post-hoc calibration framework for uncertainty-aware selective question answering. The proposed method reformulates selection-conditioned error control as a linear expectation constraint and applies a monotonized empirical-risk calibration procedure inspired by conformal risk control. Since the resulting instance-wise loss is generally non-monotone with respect to the acceptance threshold, our framework targets asymptotic rather than finite-sample risk control. A-CRC-QA is model-agnostic, requires no additional training, and can be combined with different uncertainty estimators. Experiments on CoQA and MedMCQA demonstrate its applicability to both open-ended and closed-ended question answering, achieving a favorable trade-off between accepted-answer reliability and answer retention compared with uncalibrated and confidence-bound-based baselines.
A comprehensive study to investigate the basic characteristics of current Positive-Unlabeled learning methods and proposes a general framework of PU learning by integrating the set-aware empirical risk with pseudo-labeling.
Yuanchao Dai, Zhengzhang Hou, C. Li et al.· Neural Information Processin...· 2 citations
C-Score, a compact framework that evaluates training behavior in three complementary spaces: prediction, feature representation, and optimization, suggests that clean accuracy alone is insufficient for evaluating SSL robustness in open-world environments, and that internal diagnostic signals are necessary for more reliable robustness assessment under unlabeled contamination.
Tsao-Lun Chen, Chicheng Fu, Han-Yi Chou et al.· 0 citations
Multi-label complementary label learning (MLCLL) is a weakly supervised paradigm that addresses multi-label learning (MLL) tasks using complementary labels (i.e., irrelevant labels) instead of relevant labels. Existing methods typically adopt an unbiased risk estimator (URE) under the assumption that complementary labels follow a uniform distribution. However, this assumption fails in real-world scenarios due to instance-specific annotation biases, making URE-based methods ineffective under such conditions. Furthermore, existing methods un-derutilize label correlations inherent in MLL. To address these limitations, we propose ComRank , a ranking loss framework for MLCLL, which encourages complementary labels to be ranked lower than non-complementary ones, thereby modeling pairwise label relationships. Theoretically, our surrogate loss ensures Bayes consistency under both uniform and biased cases. Experiments demonstrate the effectiveness of our method in MLCLL tasks. The code is available at https://github.com/JellyJamZhu/ComRank.
Jin Zhu, Yi Gao, Miao Xu et al.· Neural Information Processin...· 0 citations
