text-classification

Lev is a 4B open-source AI model based on Qwen, designed for efficient typed decision-making in a single forward pass with zero output tokens, achieving strong benchmarks for its size.

This paper proposes an explainable hate speech detection framework integrating DistilBERT embeddings, BiLSTM, and an attention mechanism, achieving high F1-scores on benchmark datasets for both binary and multi-class classification.

Jev, a structured text classification model, demonstrated the ability to process 724 live ads from 37 brands in 40 seconds for just $0.09 in tokens, achieving 216ms median latency per record through parallel processing and integration with Gemini.

GLiNER2.5-Decide is a 340M parameter English classification model for operational decisions, supporting multiple label sets in a single forward pass without prompt templates.

Open-Jev-27B-v1.1 is an open-source AI model with a LoRA adapter, achieving 85.28% accuracy on JevBench and featuring interactive demos for various tasks.

This paper introduces a validation-aware weak-supervision framework for building and evaluating suicidal ideation and mental health disclosure benchmarks on the decentralized social media platform Bluesky, leveraging AI models like Llama-3-8B and transformer-based classifiers to analyze performance and label validity.

This study examines blame attribution in the Danish Parliament from 1997 to 2026 using the BlameBERT classifier, revealing ideological asymmetries and a banana-shaped trajectory in political discourse.

This paper proposes Centroid-Guided Contrastive Loss (CGCL) for structured fraudulent job posting detection, unifying classification and clustering in latent space to achieve state-of-the-art performance.

FakeSpotter is a content- and strategy-agnostic tool designed to estimate the viral misinformation risk of textual content by measuring structural fingerprints, using repeated LLM assessments and logistic regression classifiers, with reported macro F1 scores of 0.788 for short texts and 0.793 for long texts on a labeled corpus.

This paper proposes matched-record evaluation for text classifiers in industrial settings, demonstrating that record selection significantly impacts performance metrics across maintenance, safety, and recall systems.

The paper benchmarks Naive Bayes against large language models for text classification, finding that NB remains competitive with LLMs when labeled data is available, offering higher throughput and lower energy consumption for resource-constrained environments.

The paper proposes DualMLC, a dual-branch framework that combines autoregressive and bidirectional language models for large-scale multi-label text classification, achieving state-of-the-art results on benchmarks.

RAPID introduces a reliability-aware pair importance distillation method to improve knowledge distillation efficiency and performance in text classification tasks.

This paper introduces HybridAL, a method for active learning that dynamically switches between retraining and fine-tuning based on a stabilization signal, improving efficiency and performance in text classification tasks.

The paper presents an auditable reliability layer for biomedical text classification that uses deterministic spell-correction to address OCR artifacts, improving classifier performance while ensuring safety through abstention under uncertainty.

The paper introduces MIL-BERT, an algorithm for classifying arbitrarily long texts by selecting relevant excerpts, achieving state-of-the-art results on multiple datasets with performance and explanatory guarantees.

J-Miner recovers executable decision knowledge from fine-tuned language-model classifiers by mining named concepts and learning decision rules, enabling inspection and transfer to lightweight models with high fidelity.

The paper proposes dLLM-SetScore, a training-free framework using discrete masked-diffusion language models for multi-label text classification, achieving competitive performance with minimal validation data.

This paper proposes a geometric filtering framework that selects high-quality LLM-generated samples by evaluating their Euclidean distance to real class examples in an embedding space, improving few-shot text classification performance by +2.61 percentage points over SMOTE.

This paper presents a machine learning pipeline for detecting self-introductions in legislative committee testimony, using features like bag-of-words and BERT probabilities. XGBoost achieves the best F1 score of 0.9747, improving further with BERT-augmented features.