text-classification

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.

This paper introduces a novel measure called relative parameter importance for task-agnostic, replay-free continual learning, enabling better balance between stability and plasticity by regularizing only parameters critical for past tasks while allowing others to update for backward knowledge transfer. The method is evaluated on class-incremental and domain-incremental text classification tasks.

Introduces DWT-Fusion, a training-free framework using discrete wavelet analysis of token log-probabilities for detecting LLM-generated text, achieving strong AUROC results on multiple datasets.

This paper compares expert-assigned and automatically-assigned MeSH terms as features for systematic review screening classifiers, showing that evaluation design significantly affects measured performance gaps, with canonical designs showing larger gaps that attenuate under alternative designs.

This paper presents ERGO, an error-driven method for iterative prompt optimization in text classification that diagnoses classification failures and generates targeted decision rules, achieving best accuracy on tasks where errors concentrate in specific confused label pairs.

A developer explores using classical machine learning to detect LLM-generated web fiction, creating an open-source demo and model with ~85% single-sentence accuracy.

Introduces Attention Head Reweighting (AHR), a data-efficient method for adapting LLMs to text classification tasks by learning a single scalar per attention head, drastically reducing trainable parameters while outperforming LoRA in limited data settings.

Proposes a Hierarchy-Aware RoBERTa framework for classifying cybersecurity vulnerabilities in the CWE taxonomy, demonstrating that hierarchy-aware representation learning is more effective than oversampling techniques for handling class imbalance.

A new annotated corpus of persuasion techniques in Bulgarian, Polish, and Russian, covering parliamentary debates and social media, with 25 fine-grained techniques and baseline models for detection and classification.

This paper presents an empirical study comparing how different neural architectures (MLPs, CNNs, RNNs, pretrained transformers) degrade under temporal distribution shift across image and text domains, finding that models exploiting localized features degrade fastest while pretrained encoders drift more gradually.

This paper presents a new method for structural sequence analysis using the Ladderpath approach to extract nested and hierarchical repetitions, defining three distance measures that outperform gzip-based NCD and BERT in out-of-distribution and few-shot text classification tasks, offering a lightweight and interpretable alternative.

HASE is a reinforcement-learning framework that co-evolves model weights, task solutions, and harness components (guidance and evaluation) in a unified agentic process, enabling a single 8B-parameter model to match the performance of much larger systems on text classification and alpha factor mining tasks.

RuleChef is a framework that uses LLMs to generate human-editable, executable rules for NLP tasks, iteratively improving them based on examples and human feedback, resulting in fast, deterministic, and inspectable rule systems.

This paper compares fine-tuned BERT (gbert-large) with few-shot LLM prompting (Llama 4 Maverick) for detecting threat and solution framing in German climate news sentences. BERT achieves higher F1 scores (0.83 vs 0.78), and an ablation study shows that providing preceding sentence context improves performance.

This paper evaluates confidence interval methods for classifier performance metrics under conditions typical of social science text classification, providing guidance on accurate interval estimation for metrics like recall and precision when using large language models.

Autofit2 is an automated, end-to-end pipeline for few-shot multilingual text classification built on setfit and SBERT embeddings, supporting 50+ languages with high precision from minimal labeled data.

This paper proposes a test-time adaptation approach using semi-supervised learning for AI text detection that adapts to continual distribution shifts from new LLMs, adversarial humanization, and temporal drift, outperforming state-of-the-art supervised detectors.

Pangram Labs explores the internal representations of its AI detection model Pangram 3.3.2, analyzing how the model distinguishes human vs AI text across layers using a balanced dataset of 5,000 documents from various sources.

ModTGCN is a modularity-aware graph neural network that jointly optimizes cross-entropy and a modularity-based auxiliary objective to improve text classification by leveraging global community structure in document graphs, achieving consistent gains on five benchmarks.

This paper proposes ImpSH, a triplet-based framework for implicit hate speech classification that aligns posts with implied statements and uses context-bounded semi-hard negative mining to improve cross-dataset generalization.