Search Results for author: Cuiyun Gao

Found 27 papers, 9 papers with code

VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models

no code implementations7 Dec 2023 Zongjie Li, Chaozheng Wang, Chaowei Liu, Pingchuan Ma, Daoyuan Wu, Shuai Wang, Cuiyun Gao

With recent advancements in Large Multimodal Models (LMMs) across various domains, a novel prompting method called visual referring prompting has emerged, showing significant potential in enhancing human-computer interaction within multimodal systems.

Split and Merge: Aligning Position Biases in Large Language Model based Evaluators

no code implementations29 Sep 2023 Zongjie Li, Chaozheng Wang, Pingchuan Ma, Daoyuan Wu, Shuai Wang, Cuiyun Gao, Yang Liu

Specifically, PORTIA splits the answers into multiple segments, aligns similar content across candidate answers, and then merges them back into a single prompt for evaluation by LLMs.

Language Modelling Large Language Model +1

When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection

1 code implementation21 Aug 2023 Xin-Cheng Wen, Xinchen Wang, Cuiyun Gao, Shaohua Wang, Yang Liu, Zhaoquan Gu

In this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i. e., PositIve and unlabeled Learning mOdel for vulnerability deTection.

Representation Learning Vulnerability Detection

LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types

1 code implementation12 Jun 2023 Xin-Cheng Wen, Cuiyun Gao, Feng Luo, Haoyu Wang, Ge Li, Qing Liao

(2) adaptive re-weighting module, which adjusts the learning weights for different types according to the training epochs and numbers of associated samples by a novel training loss.

Classification Representation Learning +1

MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion

no code implementations19 Dec 2022 Zi Gong, Yinpeng Guo, Pingyi Zhou, Cuiyun Gao, Yasheng Wang, Zenglin Xu

On the other hand, there are few studies exploring the effects of multi-programming-lingual (MultiPL) pre-training for the code completion, especially the impact on low-resource programming languages.

Code Completion

A Survey on Natural Language Processing for Programming

no code implementations12 Dec 2022 Qingfu Zhu, Xianzhen Luo, Fang Liu, Cuiyun Gao, Wanxiang Che

Natural language processing for programming aims to use NLP techniques to assist programming.

Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling

1 code implementation11 Oct 2022 Yuanhang Yang, shiyi qi, Chuanyi Liu, Qifan Wang, Cuiyun Gao, Zenglin Xu

Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI).

Answer Selection Natural Language Inference +2

Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation

no code implementations28 Sep 2022 Xinni Zhang, Yankai Chen, Cuiyun Gao, Qing Liao, Shenglin Zhao, Irwin King

Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention.

Knowledge Graphs

No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence

1 code implementation24 Jul 2022 Chaozheng Wang, Yuanhang Yang, Cuiyun Gao, Yun Peng, Hongyu Zhang, Michael R. Lyu

Besides, the performance of fine-tuning strongly relies on the amount of downstream data, while in practice, the scenarios with scarce data are common.

Code Summarization Code Translation

Generating Tips from Song Reviews: A New Dataset and Framework

no code implementations14 May 2022 Jingya Zang, Cuiyun Gao, Yupan Chen, Ruifeng Xu, Lanjun Zhou, Xuan Wang

However, reviews of music songs are generally long in length and most of them are non-informative for users.

HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems

no code implementations8 Apr 2022 Jiezhu Cheng, Cuiyun Gao, Zibin Zheng

Due to the complex interactions among multiple options and the high cost of performance measurement under a huge configuration space, it is challenging to study how different configurations influence the system performance.

Graph Partner Neural Networks for Semi-Supervised Learning on Graphs

no code implementations18 Oct 2021 Langzhang Liang, Cuiyun Gao, Shiyi Chen, Shishi Duan, Yu Pan, Junjin Zheng, Lei Wang, Zenglin Xu

Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification.

Graph Classification Link Prediction +1

Code Structure Guided Transformer for Source Code Summarization

no code implementations19 Apr 2021 Shuzheng Gao, Cuiyun Gao, Yulan He, Jichuan Zeng, Lun Yiu Nie, Xin Xia, Michael R. Lyu

Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance.

Code Summarization Inductive Bias +1

Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing

no code implementations23 Aug 2020 Cuiyun Gao, Jichuan Zeng, Zhiyuan Wen, David Lo, Xin Xia, Irwin King, Michael R. Lyu

Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22. 3% in terms of F1-score.

Clustering

CoreGen: Contextualized Code Representation Learning for Commit Message Generation

1 code implementation14 Jul 2020 Lun Yiu Nie, Cuiyun Gao, Zhicong Zhong, Wai Lam, Yang Liu, Zenglin Xu

In this paper, we propose a novel Contextualized code representation learning strategy for commit message Generation (CoreGen).

Representation Learning Text Generation

On the Replicability and Reproducibility of Deep Learning in Software Engineering

no code implementations25 Jun 2020 Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John Grundy, Xiaohu Yang

Experimental results show the importance of replicability and reproducibility, where the reported performance of a DL model could not be replicated for an unstable optimization process.

Feature Engineering

Why an Android App is Classified as Malware? Towards Malware Classification Interpretation

1 code implementation24 Apr 2020 Bozhi Wu, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen, Michael R. Lyu

In this paper, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal) to classify malware with high accuracy and explain the classification result meanwhile.

Android Malware Detection Classification +2

What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process

no code implementations10 Feb 2020 Jichuan Zeng, Jing Li, Yulan He, Cuiyun Gao, Michael R. Lyu, Irwin King

In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society.

Persuasiveness

Automating App Review Response Generation

1 code implementation10 Feb 2020 Cuiyun Gao, Jichuan Zeng, Xin Xia, David Lo, Michael R. Lyu, Irwin King

Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app.

Response Generation

CORE: Automating Review Recommendation for Code Changes

no code implementations20 Dec 2019 JingKai Siow, Cuiyun Gao, Lingling Fan, Sen Chen, Yang Liu

The hinge of accurate code review suggestion is to learn good representations for both code changes and reviews.

ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking

no code implementations6 Dec 2019 Shangqing Liu, Cuiyun Gao, Sen Chen, Lun Yiu Nie, Yang Liu

Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models.

Software Engineering

An Online Topic Modeling Framework with Topics Automatically Labeled

no code implementations WS 2019 Fenglei Jin, Cuiyun Gao, Michael R. Lyu

In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic.

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