6 papers · 1 filter
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…
AAAR-1.0: Assessing AI's Potential to Assist Research
Renze Lou, Hanzi Xu, Sijia Wang +15
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, a…
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…