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CTSP: Chinese Text Semantic Similarity Prediction 5

CTSP: Chinese Text Semantic Similarity Prediction

引用

5th international conference on Computer engineering and Application (ICCEA)

作者： He, Sirui Dalian Polytech Univ Dept Big Data Management & Applicat Dalian Liao Ning Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Text Similarity (TS) is a fundamental natural language processing (NLP) task with widespread applications, such as information retrieval, question answering, and dialogue systems. Among the various methods to address Text Similarity, pre-training and fine-tuning paradigm is a promising solution. However, despite its prevalence, current fine-tuned TS model relies heavily on pre-training without domain-specific knowledge, which may result in neglecting word-level information in the language without explicit word segmentation such as Chinese. Moreover, insufficient contextual information learning would result in poor semantic disambiguation in short texts. To address the challenges, We propose a framework named CTSP (Chinese Text Similarity Prediction) that enhances Chinese Text Similarity (TS) through the integration of Word Input, Self-Attention Interference, and Sentence Pairs Classification. In particular, our framework has demonstrated a notable 2% F1 score improvement on the ATEC dataset.

关键词： chinese text similarity prediction language model

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Large language Models Can Accomplish Business Process Management Tasks

Large Language Models Can Accomplish Business Process Manage...

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21st international conference on Business Process Management (BPM)

作者： Grohs, Michael Abb, Luka Elsayed, Nourhan Rehse, Jana-Rebecca Univ Mannheim Mannheim Germany

ISBN: (纸本)9783031509735;9783031509742

Business Process Management (BPM) aims to improve organizational activities and their outcomes by managing the underlying processes. To achieve this, it is often necessary to consider information from various sources, including unstructured textual documents. Therefore, researchers have developed several BPM-specific solutions that extract information from textual documents using natural language processing techniques. These solutions are specific to their respective tasks and cannot accomplish multiple process-related problems as a general-purpose instrument. However, in light of the recent emergence of Large language Models (LLMs) with remarkable reasoning capabilities, such a general-purpose instrument with multiple applications now appears attainable. In this paper, we illustrate how LLMs can accomplish text-related BPM tasks by applying a specific LLM to three exemplary tasks: mining imperative process models from textual descriptions, mining declarative process models from textual descriptions, and assessing the suitability of process tasks from textual descriptions for robotic process automation. We show that, without extensive configuration or prompt engineering, LLMs perform comparably to or better than existing solutions and discuss implications for future BPM research as well as practical usage.

关键词： Business Process Management natural language processing Large language Models ChatGPT

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A Novel Framework for AI-Integrated Electronic Manifest Generation 12

A Novel Framework for AI-Integrated Electronic Manifest Gene...

引用

12th international conference in Software engineering Research and Innovation, CONISOFT 2024

作者： Hinojosa-Palafox, Eduardo A. Rodriguez-Elias, Oscar M. De Jesus Chew Lee, Maria Teresa Segura, Claudia Sofia Garcia Tecnológico Nacional de México Depi / I. T. de Hermosillo Hermosillo Mexico

ISBN: (纸本)9798331532116

In logistics and other sectors, automation is revolutionizing our daily lives. This paper explores the field of automation in the trade of international goods: the chance to reduce errors and inefficiencies in generating the formal electronic manifest, an essential legal document for all transported goods, through state-of-the-art artificial intelligence (AI) techniques include natural language processing and machine learning for data extraction and workflow optimization, as well as image analysis models for improved product description generation. A model to improve the quality of product descriptions with image-based automated captioning and measured the performance of the model to generate the electronic manifest is presented. It was tested with a natural language processing and machine learning toolkit called BLIP, which is based on the popular supervised translation model. A model for image captioning, to improve the product description via an API. The main result of this reported work is that AI-powered solutions are likely to reduce human effort in performing many tasks for generating an electronic manifest, decrease human error rates, and expedite processing times. © 2024 IEEE.

关键词： natural language processing systems

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Automatic Short Answer Grading in the LLM Era: Does GPT-4 with Prompt engineering beat Traditional Models? 25

Automatic Short Answer Grading in the LLM Era: Does GPT-4 wi...

引用

15th international conference on Learning Analytics and knowledge

作者： Mello, Rafael Ferreira Junior, Cleon Pereira Rodrigues, Luiz Pereira, Filipe Dwan Cabral, Luciano Costa, Newarney Ramalho, Geber Gasevic, Dragan Monash Univ Melbourne Vic Australia Inst Fed Goiano Ipora Brazil Univ Fed Alagoas Comp Inst Maceio Alagoas Brazil Univ Fed Roraima Boa Vista Parana Brazil CJBG IFPE CESAR Sch NEES UFAL Jaboatao Guararapes Brazil Univ Fed Pernambuco Recife PE Brazil

ISBN: (纸本)9798400707018

Assessing short answers in educational settings is challenging due to the need for scalability and accuracy, which led to the field of Automatic Short Answer Grading (ASAG). Traditional machine learning models, such as ensemble and embeddings, have been widely researched in ASAG, but they often suffer from generalizability issues. Recently, Large language Models (LLMs) emerged as an alternative to optimize ASAG systems. However, previous research has failed to present a comprehensive analysis of LLMs' performance powered by prompt engineering strategies and compare its capabilities to traditional models. This study presents a comparative analysis between traditional machine learning models and GPT-4 in the context of ASAG. We investigated the effectiveness of different models and text representation techniques and explored prompt engineering strategies for LLMs. The results indicate that traditional machine learning models outperform LLMs. However, GPT-4 showed promising capabilities, especially when configured with optimized prompt components, such as few-shot examples and clear instructions. This study contributes to the literature by providing a detailed evaluation of LLM performance compared to traditional machine learning models in a multilingual ASAG context, offering insights for developing more efficient automatic grading systems.

关键词： Automatic short answer grading natural language processing Assessment LLM GPT

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A Chinese short text entity linking approach based on data enhancement and multi-dimensional feature fusion 7

A Chinese short text entity linking approach based on data e...

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7th international conference on Advanced Electronic Materials, Computers, and Software engineering, AEMCSE 2024

作者： Chen, Yu Zhang, Yue Xie, Jiangcun Xiao, Qiao School of Information Science and Engineering Chongqing Jiaotong University Chongqing400074 China School of Traffic and Transportation Chongqing Jiaotong University Chongqing400074 China

ISBN: (纸本)9781510681866

Recently, with the rapid growth of the Internet, a considerable amount of textual data has emerged, injecting vitality into natural language processing. As a result of the diverse characteristics of natural language descriptions, humans and computers often face ambiguity problems when processing natural language text, and they are unable to combine context and prior knowledge to deeply understand the deep meaning of the text, which limits further intelligent processing of textual data. Entity linking, as a basic task, aims to link the entities mentioned in the textual data to the corresponding entities in the knowledge base. However, short text entity links face difficulties such as limited contextual information, irregular expressions, and incomplete grammatical structures. Furthermore, the flexibility and syntactic diversity of the Chinese language increase the challenge of understanding short Chinese texts on a deeper level. This paper puts forward a Chinese short text entity linking approach via data enhancement and multi-dimensional feature fusion. Experimental results demonstrate that our approach overtakes the baseline model on the Chinese short text entity linking dataset, indicating that our proposed model exhibits better competence and adaptability in handling Chinese short text entity linking tasks. © 2024 SPIE.

关键词： Data assimilation

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Adversarial Attacks on Large language Models 17th

Adversarial Attacks on Large Language Models

引用

17th international conference on knowledge Science, engineering and Management (KSEM)

作者： Zou, Jing Zhang, Shungeng Qiu, Meikang Augusta Univ Sch Comp & Cyber Sci Augusta GA 30912 USA

ISBN: (纸本)9789819755004;9789819755011

Large language Models (LLMs) have rapidly advanced and garnered increasing attention due to their remarkable capabilities across various applications. However, adversarial attacks pose a significant threat to LLMs, as prior research has demonstrated their vulnerability, resulting in prediction inaccuracies. This paper offers a foundational overview of LLMs and traces their developmental trajectory. We systematically classify and compare adversarial examples on LLMs based on their perturbation units. Additionally, we scrutinize the root causes of vulnerability and explore prevalent defense approaches tailored to mitigate adversarial attacks on LLMs.

关键词： Large language Models (LLMs) natural language processing Security Adversarial Examples Adversarial Attacks

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GPTZoo: A Large-scale Dataset of GPTs for the Research Community 24

GPTZoo: A Large-scale Dataset of GPTs for the Research Commu...

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39th ACM/IEEE international conference on Automated Software engineering (ASE)

作者： Hou, Xinyi Zhao, Yanjie Wang, Shenao Wang, Haoyu Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

The rapid advancements in Large language Models (LLMs) have revolutionized natural language processing, with GPTs, customized versions of ChatGPT available on the GPT Store, emerging as a prominent technology for specific domains and tasks. To support academic research on GPTs, we introduce GPTZoo, a large-scale dataset comprising 730,420 GPT instances. Each instance includes rich metadata with 21 attributes describing its characteristics, as well as instructions, knowledge files, and third-party services utilized during its development. GPTZoo aims to provide researchers with a comprehensive and readily available resource to study the real-world applications, performance, and potential of GPTs. To facilitate efficient retrieval and analysis of GPTs, we also developed an automated command-line interface (CLI) that supports keyword-based searching of the dataset. To promote open research and innovation, the GPTZoo dataset will undergo continuous updates, and we are granting researchers public access to GPTZoo and its associated tools.

关键词： Large language Model LLM ChatGPT GPTs

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Deep Learning-Based knowledge Graph Construction for Three-Dimensional Design Specification of Power Plant engineering 24

Deep Learning-Based Knowledge Graph Construction for Three-D...

引用

6th international conference on Big Data engineering (BDE)

作者： Zhu, Jiangtao Wang, Tiankun Ma, Xinru Zuo, Chao Zhao, Junjie Luo, Quan GD Power Dev Co Ltd Beijing Peoples R China China Univ Min & Technol Sch Mech & Civil Engn Xuzhou Jiangsu Peoples R China Beijing Glory PKPM Technol Co LTD Beijing Peoples R China CHN ENERGY Investment Grp Co LTD Beijing Peoples R China Northwest Elect Power Design Inst CO LTD China Power Engn Consulting Grp Xian Peoples R China

ISBN: (纸本)9798400717857

With the rapid development of natural language processing technology, the field of knowledge engineering is gradually advancing towards a novel phase of knowledge expression and orderly data storage. The purpose of this paper is to explore the construction method of knowledge graph of 3D design specification for power plant engineering based on deep learning, and to realize the extraction of key information in the design specification and the automatic construction of knowledge graph by integrating the deep learning models such as BERT, Bi-LSTM, CRF, and so on.

关键词： Power plant engineering Building information modeling knowledge graph Deep learning Three-dimensional design specification

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Comprehensive implementation of TextCNN for enhanced collaboration between natural language processing and system recommendation

Comprehensive implementation of TextCNN for enhanced collabo...

引用

2024 international conference on Image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Xu, Xiaonan Xu, Zheng Ling, Zhipeng Jin, Zhengyu Du, Shuqian Northern Arizona University Flagstaff United States Stevens Institute of Technology HobokenNJ United States University of Sydney Sydney Australia Univeristy of California Irvine IrvineCA United States Trine University Phoenix ArizonaAZ United States

ISBN: (纸本)9781510680425

natural language processing (NLP) constitutes a pivotal domain of artificial intelligence focused on enabling computers to comprehend, process, and generate human language. Text classification, a fundamental NLP task, aims to categorize text into predefined classes. In recent years, deep learning has emerged as a dominant force across various research domains and has become a staple technology within NLP, particularly in text classification tasks. Unlike numerical and visual data, text processing underscores the need for nuanced processing capabilities. Traditional text classification methodologies typically involve preprocessing textual data and annotating samples manually to derive effective feature representations for classification using classical machine learning algorithms. This paper delves into the current landscape of deep learning applications in NLP, specifically in three core areas: text representation, sequence modeling, and knowledge representation. Furthermore, it explores the advancements and synergies facilitated by natural language processing in the realm of text classification, while also addressing challenges posed by adversarial techniques in text generation, classification, and semantic parsing. An empirical investigation into text classification tasks demonstrates the efficacy of interactive integration training, particularly in tandem with TextCNN, underscoring the pivotal role of these advancements in augmenting and refining text classification methodologies. © 2024 SPIE.

关键词： knowledge representation

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Recent Progress on Text Summarisation Based on BERT and GPT 16th

Recent Progress on Text Summarisation Based on BERT and GPT

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16th international conference on knowledge Science, engineering and Management (KSEM)

作者： Yang, Binxia Luo, Xudong Sun, Kaili Luo, Michael Y. Guangxi Normal Univ Sch Comp Sci & Engn Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Univ Cambridge Emmanuel Coll Cambridge England

ISBN: (纸本)9783031402913;9783031402920

Text summarisation is one of the essential topics in natural language processing. Pre-trained language models, especially BERT and GPT, are the most advanced methods for various natural language processing tasks;thus, many researchers have tried to use BERT and GPT for text summarisation. To facilitate further research on this topic, this paper surveys its state-of-the-art. Specifically, we summarise the topic's main research issues and BERT- and GPT-based solutions, compare these methods (especially their pros and cons), explore their applications, and discuss the challenges to future research.

关键词： natural language processing Pre-trained language model BERT GPT Text summarisation

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