Aug 2026· Discover Computing· Vol 29· 0 citations· 57 references
TL;DR
This work proposes distilling the model into a probabilistic classifier, enabling lightweight deployment without repeated LLM calls, and demonstrates that LSR improves macro-F1 scores by an average of 7.0% compared to standard zero-shot classification baselines.
Abstract
Zero-shot classification enables models to assign labels to unseen classes without task-specific training and has become increasingly effective with the advent of Large Language Models (LLMs). However, existing zero-shot classification approaches typically present all candidate labels as a flat, unstructured list within the prompt. As the number of classes grows, this formulation exacerbates well-known limitations of LLMs, including attention dilution and positional bias, ultimately degrading classification performance. We present Label Space Reduction (LSR), a test-time training method for transductive zero-shot LLM classification. LSR uses LLM-generated pseudo-labels to iteratively refine the classification label space by systematically ranking and reducing candidate classes, enabling the model to concentrate on the most relevant options. By leveraging unlabeled data with the statistical learning capabilities of data-driven models, LSR dynamically optimizes the label space representation at test time. Our experiments across seven benchmarks demonstrate that LSR improves macro-F1 scores by an average of 7.0% (up to 14.2%) with Llama-3.1-70B and 3.3% (up to 11.1%) with Claude-3.5-Sonnet compared to standard zero-shot classification baselines. To reduce the computational overhead of LSR, which requires an additional LLM call at each iteration, we propose distilling the model into a probabilistic classifier, enabling lightweight deployment without repeated LLM calls.
Multi-label classification of financial news is frequently affected by incomplete and noisy annotations, while obtaining expert-curated labels at scale is prohibitively expensive. This study proposes a weakly supervised classification framework that combines large language model (LLM) zero-shot annotation with a serial label-correction strategy. The framework first uses an LLM to generate initial weak labels and then refines them through a two-stage Correct→Clean procedure that recovers missing labels via centrality-weighted graph propagation before suppressing label noise. Systematic experiments on a financial subset of Reuters-21578 show that, under an extreme mixed-corruption setting with 80% missing labels and 15% noise labels, Correct→Clean increases the Micro-F1 from 0 to 0.6748. In an end-to-end evaluation, the proposed framework achieves a Micro-F1 of 0.8882 with reduced-dimensional features, recovering 88.69% of the performance gap to fully supervised learning. Additional experiments on the RCV1 Topics and AAPD datasets confirm that the advantage of Correct→Clean is consistently reproduced across domains and dataset sizes. These findings demonstrate that coupling LLM-generated annotations with ordered label correction offers an effective means of addressing the joint effects of missing and noisy labels, providing a promising approach to financial text classification when expert annotations are scarce.
This paper proposes a novel Partial label-based Self-training framework (PaSta) that leverages partial label learning technique to overcome the limitations of existing methods and designs a partial label-based classification model with two well-crafted loss functions to guide the model learning at both label and representation spaces.
Yujing Liu, Yixin Liu, Yu Zheng et al.· 0 citations
Early time-series classification (ETSC) aims to make accurate predictions from partially observed time series as early as possible. Although various stopping mechanisms and feature learning strategies have been developed for ETSC, most existing methods assume access to sufficient labeled training data, which may be unrealistic in applications with limited annotation. Under limited supervision, learning an additional sample-level stopping module and extracting effective classification features can both become challenging. In this paper, we propose FETERS, a few-shot ETSC framework that selects a dataset-level stopping ratio through class-wise leave-one-out (LOO) evaluation on the support set and uses a penalty-based reward function to manage the accuracy-earliness trade-off, thereby avoiding the need to train an additional stopping module. FETERS further combines Rocket-based features with frozen Chronos representations for classification. Extensive experiments on 69 public datasets spanning 14 domains show that FETERS achieves state-of-the-art (SOTA) performance in the 5-shot setting, with the highest average harmonic mean (HM) and the best HM on 38 datasets, while outperforming the current SOTA method on 44 datasets. FETERS also remains competitive in the full-shot setting, demonstrating its effectiveness in managing the accuracy-earliness trade-off.
Chen-An Tai, Yujia Wu, Vincent S. Tseng· 0 citations
Support Vector Generation is introduced, a kernel-based framework that converts a frozen language model into an interpretable, training-free classifier for zero-and few-shot learning and suggests that SVG offers a viable path toward efficient, interpretable NLP systems under compute constraints.
Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query samples from unknown classes. Transductive inference jointly observes the full unlabeled query set to improve prototype estimation, yet standard transductive updates do not distinguish known from unknown query samples, leaving prototypes vulnerable to open-set contamination. Drawing on latent-inlierness weighting and decoupled scoring for unknown-class samples, we propose a two-phase transductive method operating over a frozen audio encoder. First, each query sample is assigned a latent inlierness score that down-weights likely unknown-class samples, so that prototype refinement is driven primarily by known-class evidence. The refined prototypes are then directly optimized on a transductive loss combining support cross-entropy, inlierness-weighted conditional entropy minimization, and inlierness-weighted marginal entropy maximization, while open-set rejection uses a prior-adaptive free-energy score that adjusts its threshold with the prior proportion of unknown-class samples, decoupling detection from classification. Experiments on three audio datasets show our method achieves state-of-the-art results for few-shot open-set audio classification under multiple experimental conditions.
A novel weakly supervised (WS) learning MLTC framework consisting of a novel category word selection method, namely category word selection with significance ranking and crowd-sourcing (Cws-src), and a generic WS learning MLTC method, namely WS multilabel text classification with correlation-aware label propagation (Wmltc-clp), which estimates accurate pseudolabels by propagating them over a text correlation graph.
Ximing Li, Yiming Wang, Chenglong Hu et al.· IEEE Transactions on Neural...· 0 citations
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