Search Results for author: Qinkai Zheng

Found 11 papers, 8 papers with code

NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts

no code implementations7 May 2024 Shudan Zhang, Hanlin Zhao, Xiao Liu, Qinkai Zheng, Zehan Qi, Xiaotao Gu, Xiaohan Zhang, Yuxiao Dong, Jie Tang

To fill this gap, we propose NaturalCodeBench (NCB), a challenging code benchmark designed to mirror the complexity and variety of scenarios in real coding tasks.

ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback

no code implementations1 Apr 2024 Zhenyu Hou, Yilin Niu, Zhengxiao Du, Xiaohan Zhang, Xiao Liu, Aohan Zeng, Qinkai Zheng, Minlie Huang, Hongning Wang, Jie Tang, Yuxiao Dong

The work presents our practices of aligning LLMs with human preferences, offering insights into the challenges and solutions in RLHF implementations.

OctoPack: Instruction Tuning Code Large Language Models

3 code implementations14 Aug 2023 Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre

We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46. 2% pass@1).

Code Generation Code Repair

CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X

2 code implementations30 Mar 2023 Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang

Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.

Benchmarking Code Generation

GIPA: A General Information Propagation Algorithm for Graph Learning

1 code implementation19 Jan 2023 Houyi Li, Zhihong Chen, Zhao Li, Qinkai Zheng, Peng Zhang, Shuigeng Zhou

Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection.

Attribute feature selection +3

Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

1 code implementation8 Nov 2021 Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang

To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.

Adversarial Robustness Benchmarking +1

TDGIA:Effective Injection Attacks on Graph Neural Networks

1 code implementation12 Jun 2021 Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang

In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.

Adversarial Attack

GIPA: General Information Propagation Algorithm for Graph Learning

2 code implementations13 May 2021 Qinkai Zheng, Houyi Li, Peng Zhang, Zhixiong Yang, Guowei Zhang, Xintan Zeng, Yongchao Liu

Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.

Graph Attention Graph Learning +2

Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques

1 code implementation27 May 2020 Han Qiu, Yi Zeng, Qinkai Zheng, Tianwei Zhang, Meikang Qiu, Gerard Memmi

Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.

Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques

no code implementations20 Mar 2020 Qinkai Zheng, Han Qiu, Gerard Memmi, Isabelle Bloch

This report is about applications based on spatial-frequency transform and deep learning techniques.

Denoising

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