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Automatic Prompt Generation Based on Combinatorial Relationships in Reasoning Problems

Automatic Prompt Generation Based on Combinatorial Relations...

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natural language processing (ICNLP), international conference on

作者： Tong Wu Hao He School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China State Key Lab of Advanced Optical Communication System and Network School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

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： Accuracy Large language models Predictive models Cognition natural language processing Prompt engineering

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GBCloud: a Robust Cloud Model for processing Multi-dimensional Data Using Granular-ball Computing 23

GBCloud: a Robust Cloud Model for Processing Multi-dimension...

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8th international conference on Mathematics and Artificial Intelligence, ICMAI 2023

作者： Shi, Yuting Xia, Shuyin Chen, Zizhong College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China The department of Computer Science and Engineering University of California Riverside United States

ISBN: (纸本)9781450399982

Cloud model is an important analysis tool in knowledge engineering. However, it is mainly applied in one or two dimensional data and small-scale data because of complex fitting for its parameters, so it is not suitable to be applied in analysis in multidimensional data, which limits its application ability. In this paper, we proposed the granular-ball cloud model using granular-ball to composing the data space and generating a cloud model in each granular-ball. As the distribution in each granular-ball becomes simple, the cloud model can effectively fit the data distribution, resulting a considerable improvement of performance of cloud model in processing multi-dimensional data, especially enhancing its robustness. As an example, we use the granular-ball cloud model as an oversampling method for addressing imbalanced problems. The experimental results on some benchmark data sets demonstrate its effectiveness in comparison with many popular or the state-of-the-art *** implementation of the proposed GBCloud in programming language Python is available at https://***/syxiaa/GBCloud. © 2023 Copyright held by the owner/author(s)

关键词： Cloud computing

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A Systematic Comparative Analysis of Influence of Artificial Intelligence (AI with DS) in Developing Smart Learning System 4

A Systematic Comparative Analysis of Influence of Artificial...

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4th IEEE international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Alimov, Khujageldi Rasulov, Abdimumin Fayziyeva, Mavluda Toirova, Guli Narkabilova, Gulnoza Matchanova, Mukhabbat International Islamic Academy of Uzbekistan Tashkent Uzbekistan Karshi State University Karshi Uzbekistan Bukhara State University Department of Uzbek Linguistics and Journalism Bukhara Uzbekistan Fergana State University Department of Primary Education Fergana Uzbekistan Jizzakh Polytechnic Institute Department of Chemistry Jizzakh Uzbekistan

ISBN: (纸本)9798350360165

As the day dawns for each technology to penetrate its way to various sectors, higher education seems to be the first field at the leeway of integration, especially with Artificial Intelligence (AI) coming to play. This paper discusses the interacting dynamics of AI and higher education infrastructure with a focus on the most impacted areas of learning spaces. It explores the manner in which AI would influence the design and use of smart learning spaces, including the effectiveness within higher education institutions. AI integration into higher learning infrastructure sees a paradigm shift on a large scale, where traditional learning environments turn into dynamic and adaptive spaces. It is in this paper that we bring out and discuss in great detail the multifaceted roles that AI plays in shaping smart learning spaces. It uses AI-driven technologies, for example, machine learning algorithms, natural language processing, and intelligent tutoring systems, to enable tailormade and participative learning experiences. In addition, this paper discusses the influence of AI on the architecture and planning of learning spaces. Analyses with AI give valuable information on how spatial layouts, environmental factors, and even furniture settings may be changed to let student engagement, collaboration, and knowledge retention reach its full potential. Further, AI-based monitoring systems are adaptive enough to follow changes in learning behavior in real time, thus empowering teachers to be adaptive by redesigning pedagogy and the learning experience according to individual students' needs and interests. © 2024 IEEE.

关键词： Adversarial machine learning

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2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Modi, Anshul Dhanjal, Yuvraj Singh Larhgotra, Anamika Chandigarh University Computer Science Engineering Chandigarh India

ISBN: (纸本)9798350319200

The inclusion of semantic information into any similarity metric boosts its effectiveness and gives findings that are interpretable by humans for further inquiry. A similarity calculation approach only centered on word properties inside the text sometimes provides less precise findings. This document gives three methods that aims to focus on textual terms and incorporate semantic information into their feature vectors, thereby computing semantic similarities. These strategies are founded in both corpus and knowledge-based approaches, namely: cosine similarity using tf-idf vectors, cosine similarity employing word embeddings, and soft cosine similarity utilizing word embeddings. Among these three, cosine similarity utilizing tf-idf vectors is deemed the most proficient in spotting similarities among succinct texts. The texts found by this technology are easily comprehensible and can be easily employed in other applications for important information retrieval. © 2023 IEEE.

关键词： Corpusbased method Lemmatizing natural language processing semantic similarity Stop Words Tokenizing

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Analysis of Traditional and Deep Learning Architectures in NLP: Towards Optimal Solutions 6

Analysis of Traditional and Deep Learning Architectures in N...

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6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Subbulakshmi, T. Narayan, Prathiba Lakshmi Suthraye Gokulnath, Shreejith Suganya, R. Subramanian, Girish H. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Pennsylvania State University Information Systems School of Business Administration PA United States

ISBN: (纸本)9798350394702

In the ever-evolving landscape of natural language processing (NLP), the development of novel architectures and optimization techniques has been instrumental in advancing the field. This survey paper presents a comprehensive exploration of traditional and deep learning architectures employed in NLP, while also delving into the optimization strategies that enhance their performance. With a primary focus on surveying existing literature, the aim is to provide a holistic view of the landscape of NLP architectures and their associated optimization techniques. Additionally, a novel architecture is introduced that promises to contribute to the ongoing progress in NLP. This architecture, detailed in our full paper, combines the best practices and innovations from traditional and deep learning models to tackle NLP tasks effectively. Through this work, the aim is to contribute to the collective knowledge in NLP and facilitate future advancements in the field. © 2023 IEEE.

关键词： Deep learning

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Generative AI and Large language Models - Benefits, Drawbacks, Future and Recommendations 28th

Generative AI and Large Language Models - Benefits, Drawback...

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28th international conference on knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Håkansson, Anne Phillips-Wren, Gloria UiT The Arctic University of Norway Hansine Hansens vei 54 Tromsø9037 Norway Loyola University Maryland 4501 N. Charles Street BaltimoreMD21210 United States

natural language processing, with parsing and generation, has a long tradition. Parsing has been easier to perform than a generation but with generative artificial intelligence (a.k.a Gen AI) and large language models (abbr. LLMs), this has changed. Generative artificial intelligence is a type of artificial intelligence that uses a large data set to create something in the genre of that data set. It can generate different outputs ranging from texts, audio, objects, pictures, and paintings to videos, but also synthetic data. LLMs use deep learning and deep neural networks to train on large text corpora for recognizing and generating texts. These models are based on massive data sets, collected from databases and the web. They use transformer models to detect how elements in sequences relate to each other. This provides context support. Two well-known large language models are the Generative Pre-trained Transformer, GPT, used in ChatGPT and Bidirectional Encoder Representations from Transformers, BERT. Although LLMs have advantages, they have problems. This paper presents generative artificial intelligence and LLMs with benefits and drawbacks. Results from applying these models have shown that they can work well for accuracy in specificity, user personalization and human-computer communication but they may not provide acceptable, reliable and truthful results. For example, ethics, hallucinations and incorrect information, or misjudgments, are some major problems. The paper ends with future directions, research questions on LLMs, and recommendations. © 2024 The Authors.

关键词： Generative adversarial networks

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Public Procurement Fraud Detection and Artificial Intelligence Techniques: a Literature Review 23

Public Procurement Fraud Detection and Artificial Intelligen...

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23rd international conference on knowledge engineering and knowledge Management, EKAW-C 2022

作者： Nai, Roberto Sulis, Emilio Meo, Rosa Computer Science Department University of Turin Italy

Every year, a significant part of public and private organisations' revenues is lost to fraud. Recently, increasing digitisation has also brought more attention on organisational processes, procurement, and fraud data. Several automated methods have already been proposed to extract information from these kind of sources, including public procurement, also to develop predictive models for fraud detection. In addition, artificial intelligence techniques including machine learning, neural networks, natural language processing, and network analysis methodologies have been adopted to address the issue. This study offers a review of the most recent emerging studies on fraud detection for public organisations. Finally, it summarises the main existing research by proposing a review of current challenges in the field. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： natural language processing systems

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Big Data & Artificial Intelligence & Software engineering (ICBASE), international conference on

作者： Ya Zhou Chen Chen Guimin Huang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%.

关键词： knowledge engineering Accuracy Semantics knowledge based systems Syntactics natural language processing Software engineering

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A natural language processing System using CWS Pipeline for Extraction of Linguistic Features

A Natural Language Processing System using CWS Pipeline for ...

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2022 international conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kumar, Sandeep Solanki, Arun Department of Computer Science and Engineering School of ICT Gautam Buddha University Uttar Pradesh Greater Noida India

Understanding the rules of grammar and linguistic features is essential to understanding the context of a language, which helps to understand that language. Similarly, for natural language processing, the linguistic feature allows understanding of the language. This paper introduced how Coreference, Word-sense, and Semantic knowledge (CWS) of linguistic features work. It would improve the natural language Understanding (NLU) and natural language processing (NLP) tasks of any NLP model and NLP applications (either existing or new). This paper proposed a CWS pipeline method to enhance the efficiency and performance of NLP applications like text summarization, information retrieval, question-answer, machine reading comprehension, etc. The proposed CWS pipeline model used a pre-trained CoNLL-2012 coreference dataset extracted from the famous Ontonotes-5.0 dataset for the English language. The model implementation is done in Python language. The performance evaluation is done using the standard CoNLL-2012 coreference dataset for the English language. The coreference marked output is evaluated against the manually tagged gold standard dataset. The proposed CWS pipeline model gives 78.98% of the average F1 score on the MUC metric, 1.78% higher than the previous models' top result. CWS pipeline model performs better than existing models. © 2023 The Authors. Published by Elsevier B.V.

关键词： Pipelines

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Quadruple Extraction Algorithm for Building Temporal knowledge Graphs for Pasture 5

Quadruple Extraction Algorithm for Building Temporal Knowled...

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5th international conference on High Performance Big Data and Intelligent Systems, HDIS 2023

作者： Li, Zhongwen Tang, Dongming Zhou, Wei Huang, Qiucheng School of Computer Science and Engineering Southwest Minzu University Chengdu China Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station Southwest Minzu University Chengdu China

ISBN: (纸本)9798350394160

Temporal knowledge graphs play a crucial role in natural language processing(NLP). In contrast to well-established static knowledge graphs, temporal knowledge graphs provide temporal information, which can be incorporated into knowledge representations. Establishing a time-aware knowledge graph relevant to practical applications poses both research challenges and engineering value. This paper primarily discusses the key algorithm for constructing a temporal knowledge graph of the Qinghai-Tibet Plateau grasslands—the Quadruple Information Extraction Algorithm. This algorithm consists of two main components: a named entity recognition model and a relationship classification extraction model. Firstly, an entity recognition model based on word and word relationship classification is used to extract entities. The model achieves a high accuracy, recall, and F1 score of up to 97% on public datasets and custom datasets. Secondly, with a time-aware graph convolutional neural network relationship classification model, it is possible to further obtain relationships between head and tail entities under temporal constraints, thus achieving the final extraction of quadruple information. Experiments demonstrate that the quadruple algorithm successfully extracts quadruples from scientific literature related to the growth cycle of grassland plants on the Qinghai-Tibet Plateau, facilitating the representation of knowledge in this domain. This provides technical support for subsequent agricultural and pastoral science research and dissemination efforts. ©2023 IEEE.

关键词： Classification (of information)

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