70 citations · 72 across the 13 of their papers we have counts for
13 papers
ToolTree: Efficient LLM Agent Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning
Shuo Yang, Soyeon Caren Han, Yihao Ding +2
Large Language Model (LLM) agents are increasingly applied to complex, multi-step tasks that require interaction with diverse external tools across various domains. However, curren…
DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections
Jiwon Park, Seohyun Pyeon, Jinwoo Kim +5
Despite rapid progress in large language models (LLMs), current QA benchmarks still overlook the core challenge of real-world scientific information seeking: synthesizing multimoda…
VRD-IU: Lessons from Visually Rich Document Intelligence and Understanding
Yihao Ding, Soyeon Caren Han, Yan Li +1
Visually Rich Document Understanding (VRDU) has emerged as a critical field in document intelligence, enabling automated extraction of key information from complex documents across…
SynJAC: Synthetic-data-driven Joint-granular Adaptation and Calibration for Domain Specific Scanned Document Key Information Extraction
Yihao Ding, Soyeon Caren Han, Zechuan Li +1
Visually Rich Documents (VRDs), comprising elements such as charts, tables, and paragraphs, convey complex information across diverse domains. However, extracting key information f…
Deep Learning based Visually Rich Document Content Understanding: A Survey
Yihao Ding, Soyeon Caren Han, Jean Lee +1
Visually Rich Documents (VRDs) play a vital role in domains such as academia, finance, healthcare, and marketing, as they convey information through a combination of text, layout,…
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering
Yihao Ding, Kaixuan Ren, Jiabin Huang +2
Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly those dominated by lengthy textual content like research journal…
3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding
Yihao Ding, Lorenzo Vaiani, Caren Han +4
This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is de…
Workshop on Document Intelligence Understanding
Soyeon Caren Han, Yihao Ding, Siwen Luo +5
Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising…
Graph Neural Networks for Text Classification: A Survey
Kunze Wang, Yihao Ding, Soyeon Caren Han
Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learn…
PDFVQA: A New Dataset for Real-World VQA on PDF Documents
Yihao Ding, Siwen Luo, Hyunsuk Chung +1
Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions. We proposed a new document-based VQA da…
Form-NLU: Dataset for the Form Natural Language Understanding
Yihao Ding, Siqu Long, Jiabin Huang +4
Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form…
Dirac series of over an Archimedean field
Yihao Ding, Hongfeng Zhang
Motivated by the -cohomology and Dirac cohomology, we determine Dirac series of , and show that the spin lowest -type of any Dirac s…
V-Doc : Visual questions answers with Documents
Yihao Ding, Zhe Huang, Runlin Wang +5
We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand…