Search Results for author: Zhenchang Xing

Found 26 papers, 5 papers with code

Combining Shallow and Deep Representations for Text-Pair Classification

no code implementations ALTA 2021 Vincent Nguyen, Sarvnaz Karimi, Zhenchang Xing

Contemporary methods use fine-tuned transformer encoder semantic representations of the classification token in the text-pair sequence from the transformer’s final layer for class prediction.

Classification Decoder +2

Pandemic Literature Search: Finding Information on COVID-19

no code implementations ALTA 2020 Vincent Nguyen, Maciek Rybinski, Sarvnaz Karimi, Zhenchang Xing

Finding information related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually.

Information Retrieval Retrieval

VersiCode: Towards Version-controllable Code Generation

1 code implementation11 Jun 2024 Tongtong Wu, Weigang Wu, Xingyu Wang, Kang Xu, Suyu Ma, Bo Jiang, Ping Yang, Zhenchang Xing, Yuan-Fang Li, Gholamreza Haffari

In this paper, we introduce VersiCode, the first comprehensive dataset designed to assess the ability of large language models to generate verifiable code for specific library versions.

Code Completion Code Generation +2

Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI

no code implementations30 Nov 2023 Dawen Zhang, Boming Xia, Yue Liu, Xiwei Xu, Thong Hoang, Zhenchang Xing, Mark Staples, Qinghua Lu, Liming Zhu

The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns.

Data Poisoning Machine Unlearning

Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents

no code implementations22 Nov 2023 Qinghua Lu, Liming Zhu, Xiwei Xu, Zhenchang Xing, Stefan Harrer, Jon Whittle

Foundation models, such as large language models (LLMs), have been widely recognised as transformative AI technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities.

Language Modelling Large Language Model

Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?

no code implementations14 Sep 2023 Terry Yue Zhuo, Xiaoning Du, Zhenchang Xing, Jiamou Sun, Haowei Quan, Li Li, Liming Zhu

The correctness and unambiguity of API usage among these code models are crucial for achieving desirable program functionalities, requiring them to learn various API fully qualified names structurally and semantically.

Code Generation Knowledge Probing

Test-takers have a say: understanding the implications of the use of AI in language tests

no code implementations19 Jul 2023 Dawen Zhang, Thong Hoang, Shidong Pan, Yongquan Hu, Zhenchang Xing, Mark Staples, Xiwei Xu, Qinghua Lu, Aaron Quigley

To the best of our knowledge, this is the first empirical study aimed at identifying the implications of AI adoption in language tests from a test-taker perspective.

Fairness

Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions

no code implementations8 Jul 2023 Dawen Zhang, Pamela Finckenberg-Broman, Thong Hoang, Shidong Pan, Zhenchang Xing, Mark Staples, Xiwei Xu

In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of differential privacy, machine unlearning, model editing, and guardrails.

Machine Unlearning Model Editing +1

Enhancing Virtual Assistant Intelligence: Precise Area Targeting for Instance-level User Intents beyond Metadata

no code implementations7 Jun 2023 Mengyu Chen, Zhenchang Xing, Jieshan Chen, Chunyang Chen, Qinghua Lu

Although their capabilities of processing user intents have been developed rapidly, virtual assistants in most platforms are only capable of handling pre-defined high-level tasks supported by extra manual efforts of developers.

Source Code Data Augmentation for Deep Learning: A Survey

1 code implementation31 May 2023 Terry Yue Zhuo, Zhou Yang, Zhensu Sun, YuFei Wang, Li Li, Xiaoning Du, Zhenchang Xing, David Lo

This paper fills this gap by conducting a comprehensive and integrative survey of data augmentation for source code, wherein we systematically compile and encapsulate existing literature to provide a comprehensive overview of the field.

Data Augmentation

A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture

no code implementations9 May 2023 Qinghua Lu, Liming Zhu, Xiwei Xu, Yue Liu, Zhenchang Xing, Jon Whittle

The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted huge interest in foundation models.

Language Modelling Large Language Model

Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity

no code implementations30 Jan 2023 Terry Yue Zhuo, Yujin Huang, Chunyang Chen, Zhenchang Xing

We believe that our findings may give light on future efforts to determine and mitigate the ethical hazards posed by machines in LLM applications.

Ethics Language Modelling

Psychologically-Inspired, Unsupervised Inference of Perceptual Groups of GUI Widgets from GUI Images

1 code implementation15 Jun 2022 Mulong Xie, Zhenchang Xing, Sidong Feng, Chunyang Chen, Liming Zhu, Xiwei Xu

These principles are domain-independent and have been widely adopted by practitioners to structure content on GUIs to improve aesthetic pleasant and usability.

Towards a Roadmap on Software Engineering for Responsible AI

no code implementations9 Mar 2022 Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Zhenchang Xing

Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly.

Generating Informative CVE Description From ExploitDB Posts by Extractive Summarization

no code implementations5 Jan 2021 Jiamou Sun, Zhenchang Xing, Hao Guo, Deheng Ye, Xiaohong Li, Xiwei Xu, Liming Zhu

The extracted aspects from an ExploitDB post are then composed into a CVE description according to the suggested CVE description templates, which is must-provided information for requesting new CVEs.

Extractive Summarization Text Summarization

Brain-inspired Search Engine Assistant based on Knowledge Graph

no code implementations25 Dec 2020 Xuejiao Zhao, Huanhuan Chen, Zhenchang Xing, Chunyan Miao

However, when a query is complex, developers need to repeatedly refine the search keywords and open a large number of web pages to find and summarize answers.

Decision Making

Holistic Combination of Structural and Textual Code Information for Context based API Recommendation

no code implementations15 Oct 2020 Chi Chen, Xin Peng, Zhenchang Xing, Jun Sun, Xin Wang, Yifan Zhao, Wenyun Zhao

APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation.

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

2 code implementations12 Aug 2020 Jieshan Chen, Mulong Xie, Zhenchang Xing, Chunyang Chen, Xiwei Xu, Liming Zhu, Guoqiang Li

We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods.

Code Generation object-detection +1

Searching Scientific Literature for Answers on COVID-19 Questions

no code implementations6 Jul 2020 Vincent Nguyen, Maciek Rybinski, Sarvnaz Karimi, Zhenchang Xing

Finding answers related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually.

Retrieval

Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning

1 code implementation1 Mar 2020 Jieshan Chen, Chunyang Chen, Zhenchang Xing, Xiwei Xu, Liming Zhu, Guoqiang Li, Jinshui Wang

However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app.

Missing Labels

ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge

no code implementations WS 2019 Vincent Nguyen, Sarvnaz Karimi, Zhenchang Xing

We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA.

Natural Language Inference Question Answering

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