Xiaocheng Zhang

Xiaocheng Zhang, Zonghai Yao, Hong Yu

With the rapid advancement of Large Language Models (LLMs) and their outstanding performance in semantic and contextual comprehension, the potential of LLMs in specialized domains warrants exploration. This paper introduces the NoteAid EHR Interaction Pipeline, an innovative approach developed using generative LLMs to assist in patient education, a task stemming from the need to aid patients in understanding Electronic Health Records (EHRs). Building upon the NoteAid work, we designed two novel tasks from the patient's perspective: providing explanations for EHR content that patients may not understand and answering questions posed by patients after reading their EHRs. We extracted datasets containing 10,000 instances from MIMIC Discharge Summaries and 876 instances from the MADE medical notes collection, respectively, executing the two tasks through the NoteAid EHR Interaction Pipeline with these data. Performance data of LLMs on these tasks were collected and constructed as the corresponding NoteAid EHR Interaction Dataset. Through a comprehensive evaluation of the entire dataset using LLM assessment and a rigorous manual evaluation of 64 instances, we showcase the potential of LLMs in patient education. Besides, the results provide valuable data support for future exploration and applications in this domain while also supplying high-quality synthetic datasets for in-house system training.

Xiaocheng Zhang, Xi Wang, Yifei Lu et al.

Fact-checking benchmarks provide standardized testing criteria for automated fact-checking systems, driving technological advancement. With the surge of misinformation on social media and the emergence of various fact-checking methods, public concern about the transparency of automated systems and the accuracy of fact-checking for high infulence events has grown. However, existing benchmarks fail to meet these urgent needs and are predominantly English-centric, hindering the progress of comprehensive fact-checking. To address these issues, we introduce TrendFact, the first benchmark capable of evaluating hotspot perception ability (HPA) and all fact-checking tasks. TrendFact consists of 7,643 curated samples sourced from trending platforms and professional fact-checking datasets, as well as an evidence library containing 366,634 entries with publication dates. Additionally, to complement existing benchmarks in evaluating system explanation consistency and HPA, we propose two new metrics: ECS and HCPI. Experimental results show that current fact-checking systems face significant limitations when evaluated on TrendFact, which facilitates the development of more robust fact-checking methods. Furthermore, to enhance the capabilities of existing advanced fact-checking systems, the reasoning large language models (RLMs), we propose FactISR, a reasoning framework that integrates dynamic evidence augmentation with influence score-based iterative self-reflection. FactISR effectively improves RLM's performance, offering new insights into explainable and complex fact-checking.

Xingkang He, Xiaocheng Zhang, Wenchao Xue et al.

This paper studies the distributed state estimation problem for a class of discrete-time stochastic systems with nonlinear uncertain dynamics over time-varying topologies of sensor networks. An extended state vector consisting of the original state and the nonlinear dynamics is constructed. By analyzing the extended system, we provide a design method for the filtering gain and fusion matrices, leading to the extended state distributed Kalman filter. It is shown that the proposed filter can provide the upper bound of estimation covariance in real time, which means the estimation accuracy can be evaluated online.It is proven that the estimation covariance of the filter is bounded under rather mild assumptions, i.e., collective observability of the system and jointly strong connectedness of network topologies. Numerical simulation shows the effectiveness of the proposed filter.