Type prediction
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Benchmarks
Most implemented
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
RoBERTa: A Robustly Optimized BERT Pretraining Approach
You Only Look at Screens: Multimodal Chain-of-Action Agents
Zero-shot Entity Linking with Less Data
Neural Software Analysis
Papers
ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification
Fraudulent messages sent via Short Message Service (SMS) are increasingly obfuscated to evade cost-conscious classifiers in production systems. In Chinese SMS, attackers can exploit a wide range of carefully crafted obfu…
Type predictionConformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control
We study risk-averse decision making, in which an agent selects actions while being uncertain about the true system state. The risk is measured via optimized certainty equivalent (OCE) metrics, which generalize popular c…
Decision MakingType predictionTabuLM: Morphology-Aware Tabular Pre-training for Low-Resource Languages
We present TabuLM, the first language model pre-trained on Kinyarwanda tabular data. Kinyarwanda is a morphologically rich Bantu language spoken by over 12 million people in Rwanda, yet lacks any dedicated tabular repres…
Representation LearningType predictionTowards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment
Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems. However, unlike general image captioning, clinically reliable …
Image CaptioningType predictionConstructCIE: A Dataset for Extracting Causal Information from Construction Accident Narratives
Construction accident narratives contain rich causal information, but the evidence is often implicit, long-span, and distributed. We introduce ConstructCIE, a manually annotated dataset for Causal Information Extraction …
Information ExtractionType predictionFrom Detection to Mechanism: Cross-Attention Graph Neural Networks Enable Drug-Drug Interaction Type Prediction An Ablation Study with Acetylsalicylic Acid Validation
Predicting whether two drugs interact (binary detection) is a substantially dif- ferent task from predicting the mechanism type of that interaction (multi-class classification). This study presents a systematic ablation …
Multi-class ClassificationGraph Neural NetworkType prediction