Qiaoyu Tan

[Submitted on 13 Jun 2025] Title:TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks View a PDF of the paper titled TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks, by Qihai Zhang and 3 other authors View PDF HTML (experimental) Abstract:Inspired by the success of large language models (LLMs), there is a significant research shift from traditional graph learning methods to LLM-based graph frameworks,...

Abstract Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria.