2 citations · 2 across the 7 of their papers we have counts for
10 papers
Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice
Ruida Hu, Xinchen Wang, Xin-Cheng Wen +5
Code review is a cornerstone of software quality assurance, and recent advances in Large Language Models (LLMs) have shown promise in its automation. However, existing benchmarks f…
AXIOM: Benchmarking LLM-as-a-Judge for Code via Rule-Based Perturbation and Multisource Quality Calibration
Ruiqi Wang, Xinchen Wang, Cuiyun Gao +3
Large language models (LLMs) have been increasingly deployed in real-world software engineering, fostering the development of code evaluation metrics to study the quality of LLM-ge…
A Fine Evaluation Method for Cube Copying Test for Early Detection of Alzheimer's Disease
Xinyu Jiang, Cuiyun Gao, Wenda Huang +8
Background: Impairment of visual spatial cognitive function is the most common early clinical manifestation of Alzheimer's Disease (AD). When the Montreal Cognitive Assessment (MoC…
Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling
Trae Research Team, Pengfei Gao, Zhao Tian +12
Software issue resolution is a critical challenge in software engineering and has garnered increasing attention in recent years. With the rapid advancement of large language models…
LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models
Zhanyue Qin, Yue Ding, Deyuan Liu +7
Nowadays, Large Language Models (LLMs) have attracted widespread attention due to their powerful performance. However, due to the unavoidable exposure to socially biased data durin…
CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation
Xinchen Wang, Pengfei Gao, Chao Peng +2
Large language models (LLMs) have demonstrated strong capabilities in code generation, underscoring the critical need for rigorous and comprehensive evaluation. Existing evaluation…