Search Results for author: Jiaxin Yu

Found 5 papers, 2 papers with code

Security Code Review by LLMs: A Deep Dive into Responses

no code implementations29 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.

Defect Detection

MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings

no code implementations30 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.

Autonomous Driving Monocular 3D Object Detection +1

Latent Dynamic Networked System Identification with High-Dimensional Networked Data

no code implementations29 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.

Dimensionality Reduction

Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction

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.

Document-level Relation Extraction Relation +2

Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction

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.

Continual Learning Continual Relation Extraction +1

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