most citedLayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding

4 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks

Tianyuan Huang, Zepeng Zhu, Hangdi Xing +6

Braille plays a vital role in education and information accessibility for visually impaired individuals. However, Braille information processing faces challenges such as data scarc…

cs.AI2024

Is Cognition Consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding

Zirui Shao, Feiyu Gao, Zhaoqing Zhu +6

Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding, a rapidly growing research area with significant industrial demand. As a mult…

cs.CV2024

WebRPG: Automatic Web Rendering Parameters Generation for Visual Presentation

Zirui Shao, Feiyu Gao, Hangdi Xing +5

In the era of content creation revolution propelled by advancements in generative models, the field of web design remains unexplored despite its critical role in modern digital com…

cs.CV2024

ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data

Yufan Shen, Chuwei Luo, Zhaoqing Zhu +5

Recently, large language models (LLMs) and multimodal large language models (MLLMs) have demonstrated promising results on document visual question answering (VQA) task, particular…

cs.CV2024★ 4 cited

LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding

Chuwei Luo, Yufan Shen, Zhaoqing Zhu +3

Recently, leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However, previous works tha…

cs.CV2024

LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training

Rujiao Long, Hangdi Xing, Zhibo Yang +4

Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Recent methods solve this problem by predicting the adjacency relations o…