Imagen de Portada

Efficient Table Retrieval and Understanding with Multimodal Large Language Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xu, Zhuoyan, Fang, Haoyang, Han, Boran, Min, Bonan, Wang, Bernie, Hu, Cuixiong, Zhang, Shuai
Formato:	Preprint
Publicado:	2026
Materias:	Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation
por: Fang, Haoyang, et al.
Publicado: (2026)

When Does Multimodality Lead to Better Time Series Forecasting?
por: Zhang, Xiyuan, et al.
Publicado: (2025)

MLZero: A Multi-Agent System for End-to-end Machine Learning Automation
por: Fang, Haoyang, et al.
Publicado: (2025)

ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
por: He, Zelin, et al.
Publicado: (2026)

Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
por: Xu, Zhuoyan, et al.
Publicado: (2024)

Learning to Inference Adaptively for Multimodal Large Language Models
por: Xu, Zhuoyan, et al.
Publicado: (2025)

SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
por: He, Zelin, et al.
Publicado: (2026)

Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models
por: Xu, Yichen, et al.
Publicado: (2026)

Bridging Remote Sensors with Multisensor Geospatial Foundation Models
por: Han, Boran, et al.
Publicado: (2024)

Revealing Multimodal Causality with Large Language Models
por: Li, Jin, et al.
Publicado: (2025)

Why Larger Language Models Do In-context Learning Differently?
por: Shi, Zhenmei, et al.
Publicado: (2024)

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
por: Tang, Zhiqiang, et al.
Publicado: (2024)

Dynamic Adversarial Reinforcement Learning for Robust Multimodal Large Language Models
por: Bao, Yicheng, et al.
Publicado: (2026)

Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
por: Baby, Dheeraj, et al.
Publicado: (2025)

Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers
por: Liang, Yingyu, et al.
Publicado: (2024)

Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models
por: Wang, Xin, et al.
Publicado: (2025)

SinkRouter: Sink-Aware Routing for Efficient Long-Context Decoding in Large Language and Multimodal Models
por: Liu, Junnan, et al.
Publicado: (2026)

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
por: Liang, Zi, et al.
Publicado: (2025)

Effectively Steer LLM To Follow Preference via Building Confident Directions
por: Song, Bingqing, et al.
Publicado: (2025)

Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
por: Fang, Zihan, et al.
Publicado: (2024)

From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
por: Wang, Fei, et al.
Publicado: (2024)

A Step Toward Federated Pretraining of Multimodal Large Language Models
por: Xiong, Baochen, et al.
Publicado: (2026)

DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution
por: Yue, Yang, et al.
Publicado: (2024)

VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models
por: Xu, Hefei, et al.
Publicado: (2026)

Routing-Based Continual Learning for Multimodal Large Language Models
por: Mohta, Jay, et al.
Publicado: (2025)

SUA: Stealthy Multimodal Large Language Model Unlearning Attack
por: Zhang, Xianren, et al.
Publicado: (2025)

Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
por: Xu, Ruihan, et al.
Publicado: (2026)

Can Large Language Models Understand Intermediate Representations in Compilers?
por: Jiang, Hailong, et al.
Publicado: (2025)

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
por: Zhang, Longteng, et al.
Publicado: (2026)

Out-of-distribution generalization via composition: a lens through induction heads in Transformers
por: Song, Jiajun, et al.
Publicado: (2024)

Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge
por: Hu, Senkang, et al.
Publicado: (2025)

LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models
por: Wei, Songtao, et al.
Publicado: (2026)

Hyperbolic Learning with Multimodal Large Language Models
por: Mandica, Paolo, et al.
Publicado: (2024)

Evo: Autoregressive-Diffusion Large Language Models with Evolving Balance
por: Wu, Junde, et al.
Publicado: (2026)

KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
por: Zhang, Songming, et al.
Publicado: (2026)

GUNDAM: Aligning Large Language Models with Graph Understanding
por: Ouyang, Sheng, et al.
Publicado: (2024)

TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
por: Wang, Jiahao, et al.
Publicado: (2024)

RAP: Retrieval-Augmented Personalization for Multimodal Large Language Models
por: Hao, Haoran, et al.
Publicado: (2024)

Do Large Language Models (LLMs) Understand Chronology?
por: Wongchamcharoen, Pattaraphon Kenny, et al.
Publicado: (2025)

Test-Time Warmup for Multimodal Large Language Models
por: Rajaneesh, Nikita, et al.
Publicado: (2025)