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SymboleoNLP: A Tool for Generating Formal Specifications from Legal Contract Templates 32

SymboleoNLP: A Tool for Generating Formal Specifications fro...

引用

32nd ieee international Requirements engineering conference (RE)

作者： Meloche, Regan Amyot, Daniel Mylopoulos, John Univ Ottawa Sch EECS Ottawa ON Canada

ISBN: (纸本)9798350395129;9798350395112

SymboleoNLP is a Web-based tool that allows contract authors to make customizations to a legal contract template using a controlled, yet expressive, natural language. The tool also maintains a formal specification of the contract in Symboleo, a formal specification language designed for the legal contract monitoring domain. The controlled customizations allow for the automated formalization of the customized contract, enabling Symboleo-based property verification and code generation of monitoring smart contracts. This work pushes the boundaries of requirements-based contract template customization with a view towards full formalization.

关键词： contract template formalization legal requirements natural language processing Symboleo

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SKILLNET-X: A MULTILINGUAL MULTITASK MODEL WITH SPARSELY ACTIVATED SKILLS 49

SKILLNET-X: A MULTILINGUAL MULTITASK MODEL WITH SPARSELY ACT...

引用

49th ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Feng, Zhangyin Dai, Yong Zhang, Fan Tang, Duyu Feng, Xiaocheng Wu, Shuangzhi Qin, Bing Cao, Yunbo Shi, Shuming Harbin Inst Technol Harbin Peoples R China Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Traditional multitask learning methods typically can only leverage shared knowledge within specific tasks or languages, resulting in a loss of either cross-language or cross-task knowledge. This paper proposes a general multilingual multitask model, named SkillNet-X, which enables a single model to tackle many different tasks from different languages. To this end, we define several language-specific skills and task-specific skills, each of which corresponds to a skill module. SkillNet-X sparsely activates parts of the skill modules which are relevant to eitherthe target task or the target language. Acting as knowledge transit hubs, skill modules are capable of absorbing task-related knowledge and language-related knowledge consecutively. We evaluate SkillNet-X on eleven natural language understanding datasets in four languages. Results show that SkillNet-X performs better than task-specific and two multitask learning baselines. To investigate the generalization of our model, we conduct experiments on two new tasks and find that SkillNet-X significantly outperforms baselines.

关键词： natural language understanding multitask model multilingual model sparsely activated model

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AcawebAgent: A Large language Model-Powered Assistant for Early Academic Research 5

AcawebAgent: A Large Language Model-Powered Assistant for Ea...

引用

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

作者： Yang, Yingli Wang, Xiaoting Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

ISBN: (纸本)9798350386783;9798350386776

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies, particularly in large language models (LLMs) like the GPT series, has significantly impacted research and industrial applications. These models excel in various natural language processing (NLP) tasks, including text generation, comprehension, and translation. However, harnessing these capabilities for academic research still presents challenges, particularly for early-career researchers navigating extensive literature. In this paper, we introduce AcawebAgent, an inventive AutoAgent specifically crafted to enhance the abilities of beginner researchers. It leverages the advanced generation and analysis capabilities of large language models (LLMs) to collect open academic knowledge from the web. AcawebAgent offers customized research reports that include in-depth overviews, practical applications, the latest developments, and future trajectories of specific research domains, thereby significantly diminishing the time and effort needed for comprehensive literature reviews and trend analyses.

关键词： component Large language models AutoAgent Web search Early academic research

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A Review of Methods Using Large language Models in News Recommendation Systems 27

A Review of Methods Using Large Language Models in News Reco...

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27th ieee/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Li, Xinmiao Feng, Shuang Zhang, Xin Commun Univ China Sch Comp & Cyber Sci Beijing Peoples R China

ISBN: (纸本)9798350391961;9798350391954

Large language Models (LLMs) are widely used in natural language processing tasks due to their powerful semantic understanding and knowledge integration capabilities. Numerous existing recommendation studies consider recommendation tasks as a type of natural language processing, and thus LLMs have consequently brought new changes to the recommendation system paradigm. Existing research on recommendations using LLMs partly utilizes their rich data information, fine-grained user profiling, and expanded recommendation content to improve recommendation effectiveness. Additionally, some and partly studies directly uses LLMs to implement a generative recommendation paradigm. This paper adopts the literature review method to systematically sort out the current research status of news recommendation based on LLMs and classifies and summarizes the relevant research. To comprehensively understand the research in the field of news recommendation using LLMs, this paper introduces the current major work in the field of news recommendation from the two categories of generative LLM-assisted recommendation and direct generative recommendation and summarizes the current work as well as the potential future research directions and challenges.

关键词： News Recommendation Large language Models Generative News Recommendation

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The Accuracy of Domain Specific and Descriptive Analysis Generated by Large language Models 48

The Accuracy of Domain Specific and Descriptive Analysis Gen...

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48th Annual ieee international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Otieno, Denish Omondi Abri, Faranak Siami-Namini, Sima Namin, Akbar Siami Texas Tech Univ Lubbock TX 79409 USA San Jose State Univ San Jose CA 95192 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9798350376975;9798350376968

Large language models (LLMs) have attracted considerable attention as they are capable of showcasing impressive capabilities generating comparable high-quality responses to human inputs. LLMs, can not only compose textual scripts such as emails and essays but also executable programming code. Contrary, the automated reasoning capability of these LLMs in performing statistically-driven descriptive analysis, particularly on user-specific data and as personal assistants to users with limited background knowledge in an application domain who would like to carry out basic, as well as advanced statistical and domain-specific analysis is not yet fully explored. More importantly, the performance of these LLMs has not been compared and discussed in detail when domain-specific data analysis tasks are needed. Additionally, the use of LLMs in isolation is often at times insufficient for creating powerful applications and the real potential comes when LLMs are combined with other sources of computation such as LangChain. This study, consequently, explores whether LLMs can be used as generative AI-based personal assistants to users with minimal background knowledge in an application domain infer key data insights. To demonstrate the performance of the LLMs, the study reports a case study through which descriptive statistical analysis, as well as natural language processing (NLP) based investigations, are performed on a number of phishing emails with the objective of comparing the accuracy of the results generated by LLMs to the ones produced by analysts. The experimental results show that LangChain and the Generative Pre-trained Transformer (GPT-4) excel in numerical reasoning tasks i.e., temporal statistical analysis, achieve competitive correlation with human judgments on feature engineering tasks while struggle to some extent on domain specific knowledge reasoning, where domain-specific knowledge is required.

关键词： Large language Models LangChain Generative Pre-trained Transformer natural language processing Phishing Emails

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Extraction of Subjective Information from Large language Models 48

Extraction of Subjective Information from Large Language Mod...

引用

48th Annual ieee international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Kobayashi, Atsuya Yamaguchi, Saneyasu Kogakuin Univ Grad Sch Elect Engn & Elect Tokyo Japan Kogakuin Univ Tokyo Dept Informat & Commun Engn Tokyo Japan

ISBN: (纸本)9798350376975;9798350376968

Large language Models (LLMs) have been advancing natural language processing technologies for several years. Especially in the last year, generative AI (Artificial Intelligence) models have improved remarkably and attracted attention. Then, there has been a great discussion about these models, such as extracting the knowledge in these models. However, these existing works were mainly for the extraction of objective information. In this paper, we study the extraction of subjective information from LLMs by focusing on GPT, Gemini Pro, and Claude2. We then show that such information extraction can be severely limited in LLMs, but that information extraction is possible from some models.

关键词： large language model (LLM) generative AI natural language processing (NLP) pre-trained language model GPT Gemini Claude2

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Automatic Prompt Generation Based on Combinatorial Relationships in Reasoning Problems 6

Automatic Prompt Generation Based on Combinatorial Relations...

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6th international conference on natural language processing (ICNLP)

作者： Wu, Tong He, Hao Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn State Key Lab Adv Opt Commun Syst & Network Shanghai Peoples R China

ISBN: (纸本)9798350349122;9798350349115

The effectiveness of Large language Models (LLMs) in tasks involving reasoning is significantly influenced by the structure and formulation of the prompts, contemporary research in prompt engineering aims to help LLMs better understand the paradigms of reasoning question (e.g., CoT). However, these efforts have either struggled to effectively incorporate external knowledge into single prompt or integrating entire corpus information, often fails to significantly enhance the reasoning capabilities of LLMs. This paper introduces a novel prompting method that incorporates implicit hints that represent logical combinatorial relationships between known conditions in reasoning problems, guiding LLMs to think correctly in the initial steps of reasoning for such problems. Extensive and comprehensive experiment results on four different reasoning problem datasets indicate that our proposed method improved accuracy while maintaining efficiency.

关键词： Large language Models Prompt engineering natural language Reasoning

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natural language processing Based Intrusion Detection Interface For IoT Devices

Natural Language Processing Based Intrusion Detection Interf...

引用

2024 ieee international conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Hegde, Ramakrishna Keerthana, S. Sripriya, R.M. Raghu, Sreesh Kavin, K.V. Vidyavardaka College of Engineering Affiliated to Visvesvaraya Technological University Mysuru India Vidyavardaka College of Engineering Mysuru India

ISBN: (纸本)9798350388602

The 'natural language processing based intrusion detection interface for IoT devices' project aims to develop a system to detect intrusions (security threats) in Internet of Things (IoT) devices that have been compromised. The technology analyzes data from Internet of Things (IoT) devices using natural language processing (NLP), making it easier and simpler to identify and explain threats. One big challenge with IoT devices is that they generate a lot of different types of data, which makes it hard in terms of threat detection. Our project solves this by processing this data using natural language processing (NLP) to process this data and provide clear, easy-to-understand instructions for users to respond to potential security threats. This way, even those without technical expertise can understand the system's alerts and take action. The system is designed to operate on low-power Internet of Things(IoT) devices, which means it can function efficiently without consuming a lot of resources. Additionally, it will continue to pick up knowledge from the material it examines, keeping it informed about emerging risks. All things considered, the technology offers consumers effective and straightforward IoT device protection (Explainable IoT IDS) © 2024 ieee.

关键词： Network intrusion

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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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Multi-Perspective Transfer Learning for Automatic MOS Prediction of Low Resource language

Multi-Perspective Transfer Learning for Automatic MOS Predic...

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28th international conference on Asian language processing (IALP)

作者： Yin, Pengkai Liu, Rui Bao, Feilong Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9798331540869;9798331540852

Mean Opinion Score (MOS) prediction is the task to automatically evaluate synthesized speech by a neural network that emulates a human listening test. Traditional automatic MOS prediction typically focused on mainstream languages, such as English, due to large available data. However, for low-resource languages, there is no large-scale MOS prediction data, that hinders the study of those languages. In this paper, we propose a novel Multi-Perspective Transfer Learning (MPTL) training scheme with a new small-scale Mongolian MOS prediction dataset MonMOS. MPTL includes Feature Transfer and Model Transfer to transfer knowledge from the mainstream languages to low-resource language from different perspectives. The experimental results on the MonMOS show that the MPTL outperforms the standard direct training scheme with classical architecture. We will release the pre-trained models and MonMOS dataset at: https://***/Ai-S2-Lab/MPTL-MOS.

关键词： MOS Prediction Low-Resource language Transfer Learning

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