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检索条件"机构=Shanghai Development Center of Computer Software Technology"

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

引用

11th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 IEEE.

关键词： Natural language processing systems

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Automated Functionality and Security Evaluation of Large Language Models 9

Automated Functionality and Security Evaluation of Large Lan...

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9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Ding, Minjie Shen, Ying Chen, Mingang Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350389500

Natural language processing (NLP) is rapidly developing. A series of Large Language Models (LLMs) have emerged, represented by ChatGPT, which have made significant breakthroughs in natural language understanding and generation, enabling fluent dialogue with humans, understanding human intentions, and completing complex tasks. However, in addition to the fairness and toxicity of traditional language models, some new problems, including hallucination, have also emerged in LLMs, making them hard to use. Evaluating LLMs manually is challenging due to subjectivity and inefficiency. In this paper, we focused on the fuzzy matching, toxicity detection, and hallucination detection in the evaluation of LLMs automatically, and fine-tune the Mixtral-8x7B Model, which can be deployed in private cloud environment, and prove the effectiveness of our method through experiments. © 2024 IEEE.

关键词： Toxicity

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IVIFUZZER: A Fuzzing method for vulnerability exploitation on the operating interface of the In-Vehicle Infotainment system 5

IVIFUZZER: A Fuzzing method for vulnerability exploitation o...

引用

5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Wu, Jianhua Zhang, Xin Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9798350385557

As an important information system of intelligent and connected vehicles, most In-Vehicle Infotainment systems are set up and manage the vehicles through user interaction. This paper presents a Fuzzing method IVIFUZZER for vulnerability exploitation on the operation interface of the In-Vehicle Infotainment system. With the help of automated robotic arm and improved OCR technology, collecting system leakage information and directional detection of the identified input points, 6 zero day vulnerabilities were found. The experimental results show that the proposed IVIFUZZER method is effective. © 2024 IEEE.

关键词： Vehicles

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RSChat:Intelligent Question Answering Model for Railway Safety Knowledge 24

RSChat:Intelligent Question Answering Model for Railway Safe...

引用

24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Li, Jinyu Li, Chao Niu, Sen Dai, Bingrong School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350376647

The release of ChatGPT heralds a new era in artificial intelligence, as large language models (LLMs) empower myriad industries. This paper introduces RSChat, a model based on supervised fine-tuning of multiple large language models (LLMs), exploring a novel paradigm for applying knowledge in railway safety. Initially, we perform prompt engineering using collected domain data, including key information extraction and knowledge expansion. Subsequently, leveraging the constructed fine-tuning datasets, we conduct supervised fine-tuning using multiple large language models (LLMs) as base models. Finally, experimental evaluations demonstrate that RSChat can generate more professional responses in the railway safety domain, exhibiting robust potential for application. © 2024 IEEE.

关键词： Railroad accidents

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Research on Measuring Method of Function Size of Information System 26

Research on Measuring Method of Function Size of Information...

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26th ACIS International Winter Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Chunye, Du Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing &evaluating Shanghai China

ISBN: (纸本)9798350345865

With the rapid development of software industry, a variety of new technologies are produced. Research and development in cloud computing, big data, artificial intelligence, the Internet of Things, blockchain, and other related fields continue to prosper. The increasing lines of code, function points, escalating scale of software systems, and the rising complexity of software development collectively complicate software cost estimation. This article studies how to estimate the size, workload, and cost of an information system using functional scale measurement methods. © 2023 IEEE.

关键词： Information systems

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Analysis for Microservice Architecture Application Quality Model and Testing Method 26

Analysis for Microservice Architecture Application Quality M...

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26th ACIS International Winter Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Gong, Jiayu Cai, Lizhi Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai China

ISBN: (纸本)9798350345865

In recent years, microservice architectures have benefited from low coupling and high cohesion characteristics, allowing flexible on-demand deployment of complex applications and simplifying the difficulty of developing and maintaining large software systems. However, there is limited research on quality models and testing methods for microservice architectures, which leads to the problem that microservice architecture applications are difficult to test and evaluate. This paper analyzes the main problems when microservice architecture applications face quality measurement and software testing, further improves and refines the quality model of microservice architecture applications on the basis of the existing ISO software product quality model, and introduces testing methods for microservice architecture applications, which can provide reference for the construction of microservice architecture applications. © 2023 IEEE.

关键词： software testing

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An Approach to Optimize Symbolic Execution in Ethereum Smart Contracts 24

An Approach to Optimize Symbolic Execution in Ethereum Smart...

引用

24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Liu, Yi Cai, Lizhi Chai, Chenchen Ma, Yuan School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai China

ISBN: (纸本)9798350365658

As the application of smart contracts in blockchain technology becomes increasingly widespread, their security issues have emerged as a focal point of both research and practice. Although symbolic execution technology has been extensively applied for the detection of security vulnerabilities in smart contracts, its limitations in terms of execution efficiency and code coverage remain significant. In response to these challenges, this study introduces an innovative method named HoneyToken-DetectorRev, which optimizes traditional symbolic execution through program slicing technology, aiming to enhance the efficiency and effectiveness of smart contract security detection. By selecting ten smart contracts from the Ethereum platform, which vary in business logic and complexity, we conducted an empirical analysis of HoneyToken-DetectorRev. The experimental results demonstrate that this method significantly improves the efficiency of smart contract detection without sacrificing precision. This research not only provides an effective technical approach for the security detection of smart contracts but also offers theoretical and practical guidance for further enhancing the security of blockchain applications. © 2024 IEEE.

关键词： Smart contract

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Named entity recognition method of blockchain patent text based on deep learning 3

Named entity recognition method of blockchain patent text ba...

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3rd International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2024

作者： Yang, Guangguang Niu, Sen Dai, Bingrong Zhang, Bofeng Li, Chao Jiang, Yangjing School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9781510680449

As blockchain technology research continues to advance, named entity recognition within blockchain patent texts has emerged as a fundamental cornerstone for various other tasks. In light of the absence of a publicly accessible corpus within the current realm of blockchain technology, as well as the challenges posed by insufficient text semantic expression, the dearth of semantic features, and the intricacies associated with capturing diverse word vector representations. This paper us of annotated blockchain patent texts and proposes the MBBC MacBert-BiLSTM-CRF MBBC model for named entity recognition in blockchain patents. The model employs embedding the feature layer to vectorize the input text, enriching text information and mitigating polysemy issues. Text feature information is acquired through the long short-term memory network, and the conditional random field model is integrated to enforce label relationships, yielding an optimal label sequence. In this study, the accuracy rate of BAT (Blockchain Application technology, BAT) on the self-constructed corpus achieves 91%, with a recall rate of 96% and an Fl value of 93%, representing a notable enhancement compared to mainstream models. Ablation experiments further demonstrate that the model in this paper effectively performs named entity recognition tasks associated with blockchain technology and subject content. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Blockchain

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Cross-modality Complementary Interaction Network 8

Cross-modality Complementary Interaction Network

引用

8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Cao, Zhengnan Gao, Yue Shanghai Institute of Technology School of Science Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350369564

Salient Object Detection (SOD) aims to identify the most conspicuous objects and regions in the human vision. This paper presents the Cross-Modality Complementary Interaction Network(CCINet), a novel approach for obtaining complementary features between different modalities. We introduce feature complementarity Module (FCM) to capture inter-modality complementary features. Subsequently, the Multi-Scale Aggregation Module (MAM) is utilized to acquire multilevel information across different resolutions. Finally, we employ a decoder to generate saliency maps. Experimental results demonstrate the superiority of our model over previous methods, as it outperforms them across four evaluation indicators on five challenging datasets. © 2023 IEEE.

关键词： Object detection

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Evaluation of Hallucination and Robustness for Large Language Models 23

Evaluation of Hallucination and Robustness for Large Languag...

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23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Hu, Rui Zhong, Junhao Ding, Minjie Ma, Zeyu Chen, Mingang Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China Shanghai AI Industry Association Shanghai China

ISBN: (纸本)9798350359398

As large language models (LLMs) rapidly advance, rigorous testing and evaluation of these models grows increasingly crucial. To address this need, we have developed three types of questions: Chinese contextual, English contextual, and language context-independent. Testing in both Chinese and English probes the LLMs' hallucination tendencies. We investigate the impact of language on hallucinations from two perspectives: The type of language used in the input prompt and the cultural context underlying the prompt's content. Additionally, 52 multi-domain single-choice questions from C-EVAL are presented in original and randomized order to assess robustness to perturbations. Among the five LLMs, the tests demonstrate GPT-4 has the strongest anti-hallucination and robustness capabilities, answering with greater accuracy, consistency, and reliability. ChatGLM ranks second and outperforms GPT-4 on Chinese context-dependent questions. Emergent testing phenomena are analyzed from the user's perspective. Hallucinated responses are categorized and potential causal factors leading to hallucination and fragility are examined. Based on these findings, viable avenues for improvement are proposed. © 2023 IEEE.

关键词： Computational linguistics

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