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Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling
arXiv:2606.05927v1 Announce Type: new Abstract: The complex imbalanced label distribution poses a crucial challenge to multi-label classification, as most classifiers are biased towards the majority class and high-frequent labels. Oversampling is an efficient and flexible solution that augments instances to provide a more balanced training dataset for multi-label classifiers. Most existing oversampling methods create synthetic instances in a heuristic way that essentially relies on...
Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift
Announce Type: replace Abstract: Soft labels from teacher models are a de facto practice for knowledge transfer and large-scale dataset distillation (e.g., SRe2L, LPLD). However, when we limit the number of crops per image to reduce the substantial cost of storing precomputed soft labels, these methods suffer severely from local semantic drift: visually ambiguous crops can cause soft supervision to deviate from the image-level ground-truth semantics, leading to persistent errors and a...
Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models
arXiv:2606.04109v2 Announce Type: replace Abstract: Context-augmented language model systems often wrap supplied content with labels such as Reference:, Evidence:, Instruction:, Note:, or Example:, but the effect of these labels on reader-model behavior remains underexplored. We introduce a paired fixed-content probe over 500 MMLU-Pro items: each item receives the same misleading answer-bearing assertion under different discourse-role labels, and adoption is measured by whether the model...
Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models
arXiv:2606.04109v1 Announce Type: new Abstract: Context-augmented language model systems often wrap supplied content with labels such as Reference:, Evidence:, Instruction:, Note:, or Example:, but the effect of these labels on reader-model behavior remains underexplored. We introduce a paired fixed-content probe over 500 MMLU-Pro items: each item receives the same misleading answer-bearing assertion under different discourse-role labels, and adoption is measured by whether the model outputs...
Towards Label-Noise Resistant Learning via Optimal Brain Damage Masking
arXiv:2508.09697v3 Announce Type: replace Abstract: Noisy labels are inevitable in real-world scenarios. Due to the strong capacity of deep neural networks to memorize corrupted labels, these noisy labels cause significant performance degradation. Existing noise-robust methods have mainly focused on robust loss functions and sample selection, with comparatively limited exploration of dynamic architectural adaptation.
Brits want new AI labelling laws as Nigel Farage deepfake sparks scam warning
Brits want new AI labelling laws as Nigel Farage deepfake sparks scam warning EXCLUSIVE: New polling shows three quarters of Brits want compulsory labeling for AI-generated images on social media as fears grow that hyper-realistic clips are misleading users Three quarters of Brits want mandatory labelling of AI images to stop people being deceived, new research shows. Ministers face calls to bring in the legal requirement as a first step to tackling the AI "slopocalypse” on social media.
How Far Do Auto-Interpretation Labels Generalize: A Controlled Study Across Languages, Scripts, and Rewordings
Announce Type: replace Abstract: Sparse autoencoder (SAE) features are increasingly used to interpret language models, with auto-generated natural-language labels serving as the primary interface for understanding what each feature represents. We ask whether these labels generalize: does a feature labeled for a concept actually track that concept across languages and scripts? Using Serbian digraphia as a controlled testbed--the same language written in both Latin and Cyrillic via...
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
arXiv:2405.03386v2 Announce Type: replace Abstract: Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that multiple annotators, e.g., crowdworkers, typically provide class labels.
Extracting accent features in spoken Brazilian Portuguese without sociolinguistic labels
arXiv:2605.30457v2 Announce Type: replace-cross Abstract: Regional accent classification in Brazilian Portuguese (pt-BR) suffers from the need for reliable labeling. While large self-supervised learning (SSL) speech models are powerful, their training pipelines dilute sociophonetic information, since accent labels are generally not reliable or are not used in training objectives. This work introduces a novel workflow for feature extraction using only acoustic labels.
Extracting accent features in spoken Brazilian Portuguese without sociolinguistic labels
Announce Type: cross Abstract: Regional accent classification in Brazilian Portuguese (pt-BR) suffers from the need for reliable labeling. While large self-supervised learning (SSL) speech models are powerful, their training pipelines dilute sociophonetic information, since accent labels are generally not reliable or are not used in training objectives. This work introduces a novel workflow for feature extraction using only acoustic labels.