12 citations · 13 across the 8 of their papers we have counts for
8 papers
Graph-based Confidence Calibration for Large Language Models
Yukun Li, Sijia Wang, Lifu Huang +1
Reliable confidence estimation is essential for enhancing the trustworthiness of large language models (LLMs), especially in high-stakes scenarios. Despite its importance, accurate…
Advancing Chart Question Answering with Robust Chart Component Recognition
Hanwen Zheng, Sijia Wang, Chris Thomas +1
Chart comprehension presents significant challenges for machine learning models due to the diverse and intricate shapes of charts. Existing multimodal methods often overlook these…
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction
Sijia Wang, Lifu Huang
We propose a multi-agent debate as optimization (DAO) system for event extraction, where the primary objective is to iteratively refine the large language models (LLMs) outputs thr…
Targeted Augmentation for Low-Resource Event Extraction
Sijia Wang, Lifu Huang
Addressing the challenge of low-resource information extraction remains an ongoing issue due to the inherent information scarcity within limited training examples. Existing data au…
A Survey of Document-Level Information Extraction
Hanwen Zheng, Sijia Wang, Lifu Huang
Document-level information extraction (IE) is a crucial task in natural language processing (NLP). This paper conducts a systematic review of recent document-level IE literature. I…
RE: Region-Aware Relation Extraction from Visually Rich Documents
Pritika Ramu, Sijia Wang, Lalla Mouatadid +2
Current research in form understanding predominantly relies on large pre-trained language models, necessitating extensive data for pre-training. However, the importance of layout s…
The Art of Prompting: Event Detection based on Type Specific Prompts
Sijia Wang, Mo Yu, Lifu Huang
We compare various forms of prompts to represent event types and develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot…
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding
Sijia Wang, Mo Yu, Shiyu Chang +2
Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols. These approaches are usually limit…