no code implementations • 29 Jan 2024 • Jiaxin Yu, Peng Liang, Yujia Fu, Amjed Tahir, Mojtaba Shahin, Chong Wang, Yangxiao Cai
To explore the challenges of applying LLMs in practical code review for security defect detection, this study compared the detection performance of three state-of-the-art LLMs (Gemini Pro, GPT-4, and GPT-3. 5) under five prompts on 549 code files that contain security defects from real-world code reviews.
no code implementations • 30 Sep 2023 • Lei Yang, Jiaxin Yu, Xinyu Zhang, Jun Li, Li Wang, Yi Huang, Chuang Zhang, Hong Wang, Yiming Li
We discover that most existing monocular 3D object detectors rely on the ego-vehicle prior assumption that the optical axis of the camera is parallel to the ground.
no code implementations • 29 Sep 2023 • Jiaxin Yu, Yanfang Mo, S. Joe Qin
Networked dynamic systems are ubiquitous in various domains, such as industrial processes, social networks, and biological systems.
1 code implementation • NAACL 2022 • Jiaxin Yu, Deqing Yang, Shuyu Tian
Compared with traditional sentence-level relation extraction, document-level relation extraction is a more challenging task where an entity in a document may be mentioned multiple times and associated with multiple relations.
Ranked #37 on Relation Extraction on DocRED
1 code implementation • ACL 2021 • Li Cui, Deqing Yang, Jiaxin Yu, Chengwei Hu, Jiayang Cheng, Jingjie Yi, Yanghua Xiao
As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the model continuously.