activity
20202024
most citedVRDU: A Benchmark for Visually-rich Document Understanding

12 citations · 54 across the 20 of their papers we have counts for

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Showing cs.CLShow all

17 papers · 1 filter

cs.CL2024★ 7 cited

Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting

Zilong Wang, Zifeng Wang, Long Le +9

Retrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date respo…

cs.CL2024★ 3 cited

MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks

Letian Peng, Zilong Wang, Feng Yao +2

Information extraction (IE) is a fundamental area in natural language processing where prompting large language models (LLMs), even with in-context examples, cannot defeat small LM…

cs.CL2024

DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering

Alex Nguyen, Zilong Wang, Jingbo Shang +1

The application of natural language processing models to PDF documents is pivotal for various business applications yet the challenge of training models for this purpose persists i…

cs.CL2024

Answer is All You Need: Instruction-following Text Embedding via Answering the Question

Letian Peng, Yuwei Zhang, Zilong Wang +4

This work aims to build a text embedder that can capture characteristics of texts specified by user instructions. Despite its tremendous potential to deploy user-oriented embedding…

cs.CL2024★ 12 cited

Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

Zilong Wang, Hao Zhang, Chun-Liang Li +8

Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verificat…

cs.CL2023★ 1 cited

EmojiLM: Modeling the New Emoji Language

Letian Peng, Zilong Wang, Hang Liu +2

With the rapid development of the internet, online social media welcomes people with different backgrounds through its diverse content. The increasing usage of emoji becomes a noti…

cs.CL2023★ 2 cited

LMDX: Language Model-based Document Information Extraction and Localization

Vincent Perot, Kai Kang, Florian Luisier +9

Large Language Models (LLM) have revolutionized Natural Language Processing (NLP), improving state-of-the-art and exhibiting emergent capabilities across various tasks. However, th…

cs.CL2023★ 6 cited

Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation

Li Zhong, Zilong Wang

Recently, the large language models (LLMs) have shown extraordinary ability in understanding natural language and generating programming code. It has been a common practice of soft…

cs.CL2023

Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path

Zilong Wang, Jingbo Shang

The rapid growth of web pages and the increasing complexity of their structure poses a challenge for web mining models. Web mining models are required to understand the semi-struct…

cs.CL2023

Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation

Prashant Krishnan, Zilong Wang, Yangkun Wang +1

Recent advances of incorporating layout information, typically bounding box coordinates, into pre-trained language models have achieved significant performance in entity recognitio…

cs.CL2022★ 12 cited

VRDU: A Benchmark for Visually-rich Document Understanding

Zilong Wang, Yichao Zhou, Wei Wei +2

Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry. Although recen…

cs.CL2022

Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition

Zihan Wang, Kewen Zhao, Zilong Wang +1

Fine-tuning pre-trained language models has recently become a common practice in building NLP models for various tasks, especially few-shot tasks. We argue that under the few-shot…

cs.CL2022

Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework

Zilong Wang, Jingbo Shang

Entity recognition is a fundamental task in understanding document images. Traditional sequence labeling frameworks treat the entity types as class IDs and rely on extensive data a…

cs.CL2021★ 2 cited

LayoutReader: Pre-training of Text and Layout for Reading Order Detection

Zilong Wang, Yiheng Xu, Lei Cui +2

Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep lear…

cs.CL2021★ 1 cited

GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding

Zilong Wang, Mingjie Zhan, Houxing Ren +4

Forms are a common type of document in real life and carry rich information through textual contents and the organizational structure. To realize automatic processing of forms, wor…

cs.CL2020

Exploring Semantic Capacity of Terms

Jie Huang, Zilong Wang, Kevin Chen-Chuan Chang +2

We introduce and study semantic capacity of terms. For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intellige…

cs.CL2020★ 1 cited

TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis

Zilong Wang, Zhaohong Wan, Xiaojun Wan

Multimodal sentiment analysis is an important research area that predicts speaker's sentiment tendency through features extracted from textual, visual and acoustic modalities. The…