Imagen de Portada

Document Understanding, Measurement, and Manipulation Using Category Theory

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Claypoole, Jared, Gong, Yunye, Yanofsky, Noson S., Divakaran, Ajay
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

BloomVQA: Assessing Hierarchical Multi-modal Comprehension
por: Gong, Yunye, et al.
Publicado: (2023)

Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models
por: Chen, Yangyi, et al.
Publicado: (2023)

DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback
por: Chen, Yangyi, et al.
Publicado: (2023)

Punching Bag vs. Punching Person: Motion Transferability in Videos
por: Abdullah, Raiyaan, et al.
Publicado: (2025)

Probing Conceptual Understanding of Large Visual-Language Models
por: Schiappa, Madeline, et al.
Publicado: (2023)

When Should a Language Model Trust Itself? Same-Model Self-Verification as a Conditional Confidence Signal
por: Phalod, Aditya Ajay
Publicado: (2026)

GLEAN: Active Generalized Category Discovery with Diverse LLM Feedback
por: Zou, Henry Peng, et al.
Publicado: (2025)

Leveraging Distillation Techniques for Document Understanding: A Case Study with FLAN-T5
por: Lamott, Marcel, et al.
Publicado: (2024)

Unsupervised Learning and Representation of Mandarin Tonal Categories by a Generative CNN
por: Schenck, Kai, et al.
Publicado: (2025)

Dynamic Subset Tuning: Expanding the Operational Range of Parameter-Efficient Training for Large Language Models
por: Stahlberg, Felix, et al.
Publicado: (2024)

Measuring Visual Understanding in Telecom domain: Performance Metrics for Image-to-UML conversion using VLMs
por: Ranjani, HG, et al.
Publicado: (2025)

FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale
por: Patel, Ajay, et al.
Publicado: (2026)

DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
por: Patel, Ajay, et al.
Publicado: (2024)

Understanding Chain-of-Thought in LLMs through Information Theory
por: Ton, Jean-Francois, et al.
Publicado: (2024)

AST-T5: Structure-Aware Pretraining for Code Generation and Understanding
por: Gong, Linyuan, et al.
Publicado: (2024)

Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification
por: Sahu, Pritish, et al.
Publicado: (2024)

Understanding Generalization in Role-Playing Models via Information Theory
por: Li, Yongqi, et al.
Publicado: (2025)

Generalized Category Discovery with Large Language Models in the Loop
por: An, Wenbin, et al.
Publicado: (2023)

Simple Mechanisms for Representing, Indexing and Manipulating Concepts
por: Li, Yuanzhi, et al.
Publicado: (2023)

Benchmarking Distilled Language Models: Performance and Efficiency in Resource-Constrained Settings
por: Wani, Sachin Gopal, et al.
Publicado: (2026)

DocAtlas: Multilingual Document Understanding Across 80+ Languages
por: Heakl, Ahmed, et al.
Publicado: (2026)

Transforming Causality: Transformer-Based Temporal Causal Discovery with Prior Knowledge Integration
por: Huang, Jihua, et al.
Publicado: (2025)

Unleashing the Potential of Model Bias for Generalized Category Discovery
por: An, Wenbin, et al.
Publicado: (2024)

Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms
por: Claypoole, Jared, et al.
Publicado: (2025)

Optimizing Large Language Model Training Using FP4 Quantization
por: Wang, Ruizhe, et al.
Publicado: (2025)

Machine Learning Research Has Outpaced Its Communication Norms and NeurIPS Should Act
por: Rangarajan, Ajay Mandyam, et al.
Publicado: (2026)

Energy Considerations of Large Language Model Inference and Efficiency Optimizations
por: Fernandez, Jared, et al.
Publicado: (2025)

Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory
por: Li, Ming, et al.
Publicado: (2025)

Compressing LLMs: The Truth is Rarely Pure and Never Simple
por: Jaiswal, Ajay, et al.
Publicado: (2023)

Machine Understanding of Scientific Language
por: Wright, Dustin
Publicado: (2025)

The Detection and Understanding of Fictional Discourse
por: Piper, Andrew, et al.
Publicado: (2024)

Towards Understanding Steering Strength
por: Taimeskhanov, Magamed, et al.
Publicado: (2026)

Understanding Addition and Subtraction in Transformers
por: Quirke, Philip, et al.
Publicado: (2024)

Architecture-Agnostic Curriculum Learning for Document Understanding: Empirical Evidence from Text-Only and Multimodal
por: Hamdan, Mohammed, et al.
Publicado: (2026)

Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models
por: Aïdasso, Henri, et al.
Publicado: (2026)

Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations
por: Ghanim, Mansour Al, et al.
Publicado: (2025)

Understanding Finetuning for Factual Knowledge Extraction
por: Ghosal, Gaurav, et al.
Publicado: (2024)

Zeroth-Order Sharpness-Aware Learning with Exponential Tilting
por: Gong, Xuchen, et al.
Publicado: (2025)

One Category One Prompt: Dataset Distillation using Diffusion Models
por: Abbasi, Ali, et al.
Publicado: (2024)

Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
por: Havrilla, Alex, et al.
Publicado: (2024)