Papers Text Classification
“Text Classification” 태그가 달린 논문 3,864편 · 필터 해제
A Comparative Explainability Framework for DeBERTa-v3 in Zero-Shot Medical Abstract Classification
A comparative explainability framework is presented to audit DeBERTa-v3 under zero-shot classification of medical abstracts. The work addresses the disagreement problem in Explainable Artificial Intelligence, where diffe…
Natural Language InferenceText ClassificationTask-Oriented Rank Adaptation for Continual Learning in Text Classification
Continual learning (CL) in text classification faces two critical challenges: catastrophic forgetting and negative transfer across sequential tasks. Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA enable effi…
parameter-efficient fine-tuningText ClassificationContinual LearningMaking Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction
Clinical language models can achieve strong in-hospital accuracy yet fail under deployment shifts because they exploit note-specific artifacts (e.g., templates, separators, boilerplate) that do not reflect patient state.…
Mortality PredictionText ClassificationHelaBERT: Enhancing Sinhala Language Understanding with Dual Pooling Classification Head
We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourced from MADLAD-400, CulturaX, and a custom corpus comprising news art…
Text ClassificationSentiment AnalysisMIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees
Many text classification decisions are viable based on constituent excerpts alone. Taking inspiration from the field of multiple instance learning, we present an algorithm for training a neural network to classify text b…
Multiple Instance LearningText ClassificationTask-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
We present a novel approach to efficient LLM harness optimization through adaptive validation task selection. Harness optimization iteratively rewrites the harness code based on validation performance, enabling substanti…
Text ClassificationFraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation
Federated fine-tuning with Low-Rank Adaptation (LoRA) enables efficient collaborative adaptation of Large Language Models (LLMs) without centralizing private data. However, LoRA's two-factor parameterization creates an a…
Text ClassificationRelative Parameter Importance in Task-Agnostic Replay-Free Continual Learning
Achieving continual learning (CL) with deep neural networks requires balancing stability and plasticity while enabling knowledge transfer. In this work, we focus on offline learning algorithms under the constraints: (I) …
Incremental LearningText ClassificationContinual LearningText GenerationLearning to Fine-tune Foundation Models under Resource Limitations
We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device. At each time slot, a new batch of training data arrives, and the controller is faced with tw…
Reinforcement LearningText ClassificationSentiment AnalysisConstraint-Aware Hierarchical Search for Regulation-Driven Fine-Grained Classification
Tasks such as customs tariff classification, export control categorization, and standards-based equipment coding require assigning an input instance to a fine-grained class under an explicit regulatory hierarchy. Unlike …
Text ClassificationSemantic SimilarityMulti-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors
Assigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual step in vulnerability analysis. We study this task as a text classifica…
Text ClassificationNeuron-Aware Active Few-Shot Learning for LLMs
Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while p…
Text ClassificationFew-Shot LearningRuleChef: Grounding LLM Task Knowledge in Human-Editable Rules
We present RuleChef, a framework that uses large language models (LLMs) to generate executable rules for NLP tasks such as text classification, Named Entity Recognition (NER), or relation extraction. Rules are generated …
Relation ExtractionText ClassificationComparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News
News media play a central role in shaping public perceptions of climate change, and whether coverage emphasizes threats or solutions has measurable effects on audience engagement and policy support. Automated detection o…
Text ClassificationEstimating Uncertainty in Classifier Performance with Applications to Large Language Models and Nested Data
Researchers increasingly use text classification--supervised models or large language models--to measure constructs from natural language, providing metrics such as recall and precision as evidence of their validity. Yet…
Text ClassificationClusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability
Pre-trained language models such as BERT achieve strong text classification performance but lack transparency, limiting their use in high-stakes settings. The Tsetlin Machine (TM) offers fully interpretable, clause-based…
Text ClassificationReinforcement Learning for Neural Model Editing
Editing pretrained neural networks requires specialized algorithms tailored to specific objectives. Designing such algorithms is often time-consuming and demands significant effort. We present an exploratory framework th…
Reinforcement LearningGeneral ClassificationImage ClassificationText ClassificationMentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection
Detecting mental health disorders from Arabic social media text remains challenging due to dialectal variation, informal language, limited high-quality annotated resources, and severe class imbalance. While English menta…
Text ClassificationFrequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation
Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only in the original token domain. We propose FLaG, a plug-in aggregation mo…
Image ClassificationText ClassificationActivity PredictionSeq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery
Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms. In this paper, we propose Seq103, a unified NEAT-style neuroevolution…
Neural Architecture SearchText Classification