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Chain-of-Factors: A Zero-Shot Prompting Methodology Enabling Factor-Centric Reasoning in Large language Models 23

Chain-of-Factors: A Zero-Shot Prompting Methodology Enabling...

引用

23rd IEEE international conference on Machine Learning and Applications, ICMLA 2024

作者： Hussain, Musarrat Ur Rehman, Ubaid Nguyen, Tri D.T. Lee, Sungyoung Kim, Seong Tae Bae, Sung-Ho Kim, Jung Uk UiT The Arctic University of Norway Department of Computer Science Tromso Norway Kyung Hee University Dept. of Computer Science & Engineering Yongin Korea Republic of Kyung Hee University Department of Computer Science and Engineering Yongin Korea Republic of

ISBN: (纸本)9798350374889

Large language models (LLMs) have significantly improved numerous natural language processing tasks. However, their performance relies heavily on the provided instructions or prompts. Recently, several prompting methodologies have been developed to enhance the reasoning abilities of LLMs. Notably, the Chain-of-Thought (CoT) approach provides examples that help break down tasks into sub-steps, resulting in more accurate solutions. However, the process of generating detailed examples may not be user-friendly, as end users prefer providing task descriptions rather than a set of examples. In this study, we introduce Chain-of-Factors (CoF), an innovative zero-shot prompting methodology that incorporates task-specific instructions as a chain of factors into the prompt, aimed at enhancing the factor-centric reasoning abilities of LLMs. Experiments on three LLMs, including ChatGPT-3.5, Gemini, and GPT-4, show performance improvements ranging from 0.01% to 40.2% in accuracy on various symbolic reasoning and logical reasoning tasks compared with zero-shot and few-shot CoT. In summary, CoF enhances LLMs' reasoning abilities by including task-specific steps and instructions, while also decreasing the necessity for fine-tuning specific to each task. © 2024 IEEE.

关键词： natural language processing systems

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Enlightening Justice: Empowering Society Through AI-driven Legal Assistance 2

Enlightening Justice: Empowering Society Through AI-driven L...

引用

2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Senthil Pandi, S. Farook, A. Mohammed Kingston, W. Kannaiah, Sathish Kumar Department of CSE Rajalakshmi Engineering College Chennai India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350383867

One of the biggest issues facing modern society is the pervasive legal ignorance that denies many people their liberties and access to fairness. In order to address this issue, this paper offers a revolutionary solution that will use cutting-edge technology to give people from all areas of life full legal advice and support. Through in-depth research of numerous legal situations and locations of crimes, this paper provides practical suggestions and technical support to equip individuals with knowledge of pertinent laws and necessary proof. The suggested approach guarantees equitable treatment for all, clarifies complicated legal issues, and acts as a lighthouse of truth for the poor and illiterate. By creating a strong framework of legal support, this method gives people the analytical skills and legal terminology knowledge they need to successfully negotiate the complexities of the legal system. Advanced methods like BERT vectorization with text representation and NLP (natural language processing) with document preprocessing and feature extraction are important parts of the system. In order to improve the effectiveness and precision of legal classification and analysis, the vectorized input is then used to train a hybrid model that combine CNN and LSTM architectures with RNN. The study also investigates the use of collaborative learning to combine the predictive strengths of several models, producing optimal performance in a range of legal contexts. With an efficiency rate of up to 85%, the system outperforms conventional techniques and provides real-time assistance catered to the particular requirements of each case by tokenizing and classifying legal documents. The suggested methodology shows the effectiveness of AI-driven techniques in enhancing legal competence and easing access to court for all members of society, drawing on information from reliable sources like Kaggle. This article proposes a compelling plan for the years to come of legal help, where the str

关键词： natural language processing systems

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A Comparative Analysis of ChatGPT-Generated and Human-Written Use Case Descriptions 24

A Comparative Analysis of ChatGPT-Generated and Human-Writte...

引用

ACM/IEEE 27th international conference on Model Driven engineering languages and Systems: Companion Proceedings (MODELS)

作者： Oguz, Evin Aslan Kuester, Jochen M. Bielefeld Univ Appl Sci Bielefeld Germany

ISBN: (纸本)9798400706226

The development of comprehensive use case descriptions is a critical task in software engineering, providing essential insights for requirement analysis and system design. The advent of advanced natural language processing models, such as ChatGPT, has sparked interest in their potential to automate tasks traditionally performed by humans, including the generation of use case descriptions in software engineering. Understanding the capabilities and limitations of ChatGPT in generating use case descriptions is crucial for software engineers. Without a clear understanding of its performance, practitioners may either overestimate its utility, leading to reliance on suboptimal drafts, or underestimate its capabilities, missing opportunities to streamline the drafting process. This paper addresses how well ChatGPT performs in generating use case descriptions, evaluating their quality compared to human-written descriptions. To do so, we employ a structured approach using established quality guidelines and the concept of "bad smells" for use case descriptions. Our study presents the first attempt to bridge the knowledge gap by offering a comparative analysis of ChatGPT-generated and human-written use case descriptions. By providing an approach to objectively assess ChatGPT's performance, we highlight its potential and limitations, offering software engineers insights to effectively integrate AI tools into their workflows.

关键词： use case description ChatGPT requirements engineering quality

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Bilingual Chatbot For Covid-19 Detection Based on Symptoms Using Rasa NLU 2

Bilingual Chatbot For Covid-19 Detection Based on Symptoms U...

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2nd international conference on Innovative Research in Applied Science, engineering and Technology (IRASET)

作者： Mellah, Youssef Bouchentouf, Toumi Rahmoun, Noureddine Rahmoun, Mohammed SupMTI SupMTI Oujda Res Lab Oujda Morocco Univ Mohammed Premier LaRSA ENSAO Oujda Morocco

ISBN: (纸本)9781665422093

Accessibility to medical knowledge and healthcare costs are the two major impediments for the common man. Conversational agents like Medical Chatbots, which are designed keeping in view medical applications can potentially address these issues. Chatbots can either be generic of specificin nature. Covid-19 is a communicable disease and early detection of it can let people know about the serious consequences of this disorder and help save human lives. In this article, we present a specific text-to-text Chatbot that engagespatients in the conversation using advanced natural language Understanding (NLU) and natural language processing (NLP) techniques using Rasa Framework, to provide a personalized prediction based on the various symptoms sought from the patient. The Chatbot handles two languages: Arabic and French, then according to the analysis result, it suggests measures and actions be taken in order to serve life and prevent the spread of this virus which has devastated the whole world.

关键词： Conversational agents Chatbots Covid-19 natural language Understanding (NLU) natural language processing (NLP) Rasa Framework

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An AI Based Chat Bot For Banking Applications using Intelligent Chat Bots

An AI Based Chat Bot For Banking Applications using Intellig...

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Kira, Ajmeera Vidhya, Veda Kamesh, D.B.K. Srilakshmi, V. Paulraj, D. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Malla Reddy Engineering College for Women maisammaguda hyderabad 500100 India B V Raju Institute of Technology India Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India

ISBN: (纸本)9798350305708

A chat and voice assistant that is powered by AI and is integrated into a banking application. This assistant translates data in such a way that the text and audio are designed to simulate human conversations. Since their inception, voice and chat assistants have seen substantial technological advancement. The advancements that have been made in machine learning and natural language processing have made it possible for them to now learn from their experiences and conduct conversations that are more human-like. Because of their more human nature, they are able to assist with a wide variety of tasks, such as calculating loan interests, confirming facts regarding transactions, and checking savings. © 2023 IEEE.

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Fine-Tuned BERT Model for Hate Speech Detection in Political Discourse 22

Fine-Tuned BERT Model for Hate Speech Detection in Political...

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22nd international conference on ICT and knowledge engineering, ICT and KE 2024

作者： Nwaiwu, Steve Jongsawat, Nipat Tungkasthan, Anucha Thaloey, Jarinya RMUTT Data and Information Science Faculty of Science and Technology Pathum Thani Thailand RMUTT Big Data Analytics and Management Faculty of Science and Technology Pathum Thani Thailand

ISBN: (纸本)9798350391411

This research tackles the challenge of detecting hate speech and offensive content in political discussions on social media. By employing natural language processing (NLP) techniques, the study aims to contribute to creating healthier online communities. The researchers started with a manually labeled dataset of Reddit comments centered on political discourse. To address the common issue of class imbalance in classification tasks, they used an oversampling strategy to ensure equal representation of each class. Additionally, they applied data augmentation, specifically synonym replacement, to increase the diversity of the training data, which helps improve the model's generalization. For text preprocessing, the Bidirectional Encoder Represen- tations from Transformers (BERT) tokenizer was used. This tokenizer converts text into subwords, which is especially useful for handling out-of-vocabulary terms. The model used in this study is a fine-tuned version of BERT base, trained with specific hyperparameters: a learning rate of 5e-5, a batch size of 24, and a weight decay of 0.01. Training was carried out over seven epochs using the Hugging Face Transformers library and NVIDIA Tesla P100 GPUs available on Kaggle. The model's performance was assessed using 5-fold cross- validation, resulting in impressive metrics: an average accuracy of 99.34 percent, precision of 99.36 percent, recall of 99.34 percent, F1-score of 99.34 percent, and an ROC-AUC score of 99.96 percent. These results highlight the model's effectiveness in identifying hate speech and offensive content across different data splits. Compared to earlier research, this approach shows significant improvement in performance metrics. It demonstrates the potential of combining BERT with data augmentation and oversampling techniques for detecting hate speech in political discourse on Reddit. This study lays the groundwork for future research into real-time detection systems and cross-lingual models. © 2024 IEEE.

关键词： natural language processing systems

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Chinese Nested Entity Recognition Benchmark in the Field of Naval Warfare

Chinese Nested Entity Recognition Benchmark in the Field of ...

引用

2024 IEEE international conference on Unmanned Systems, ICUS 2024

作者： Zhang, Shengjie Li, Qi Kou, Yueping Wang, Jun Li, Zhengwei Wu, Baolei School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (纸本)9798350384185

Named entity recognition (NER) is a fundamental task in natural language processing and a key technology for building knowledge graphs. However, the performance of NER is often limited by domain-specific characteristics. For unpopular Chinese entity recognition fields, such as naval warfare, NER typically performs poorly due to the lack of sufficient pre-trained models and labeled data. Furthermore, the naval warfare field includes numerous specialized terms and complex domain knowledge, with nested structures and physical relationships that are uncommon in conventional corpora, posing additional challenges for NER. To address these issues, we manually construct a high-quality naval warfare domain corpus. We also propose a new nested entity recognition framework, which fully leverages entity boundary information, enhances the learning of entity adjacency modeling information, and uses multi-scale convolution to obtain richer span feature representation. Experiments demonstrate the effectiveness of this method in four Chinese NER corpora. © 2024 IEEE.

关键词： Naval warfare

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Summarization for News Article Using Unsupervised Learning Techniques 1

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2nd international conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Rai, Shrishti Sharma, Deepak Department of Computer Engineering K J Somaiya College of Engineering Vidyavihar Maharashtra Mumbai India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Text summarization is one of the important tasks in natural language processing, which involves shortening of information while retaining meaning. A well-condensed summary includes key details, which is grammatically correct, and free of redundancy. With rapid growth in Internet, there is vast amount of unstructured information. This demands automatic text summarization across various fields like academia, business, and search engines. Our paper develops a text summarization system for news articles in politics, sports, and reviews. It compares summaries while maintaining the original meaning, by criteria for evaluation. This will help in enhancing productivity, reducing errors in extracting insights from English and regional language data. This extends across education, commerce, and online search platforms, offering efficient entry to valuable understandings from the complex web of data. Through evaluation, we would elevate text summarization in English and regional language for better knowledge understanding a usage. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Digital storage

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A Preliminary Work on Tenyidie Stemming Corpus Creation and Deep Learning Applications 4th

A Preliminary Work on Tenyidie Stemming Corpus Creation and...

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4th international conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Angami, Teisovi Tuithung, Themrichon Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India Department of Information Technology School of Engineering and Technology Nagaland University Lumami India

ISBN: (纸本)9789819757022

Tenyidie language belonging to the Tibeto-Burman family is one of the major languages of Nagaland in northeastern India. It is a low-resource, tonal, SOV, and a high agglutinative language. Stemming is an important natural language processing (NLP) task wherein the stem or the root word is identified and extracted. To the best of the authors knowledge, there has been no work reported on corpus creation and stemming using deep learning for the Tenyidie language. In this paper, we build an annotated corpus of 5400 manually stemmed words in Tenyidie to apply deep learning to the stemming task. We applied state-of-the-art deep learning models, such as the Encoder-decoder models, and compared our results with the rule-based approach, which uses both affix stripping and root word matching methods. Our experimental result shows that the deep learning models outperform the rule-based approach. We achieved the highest accuracy of 85.55% with Encoder-decoder (BLSTM) with attention. We also provide an analysis of the overstemmed and understemmed words in our test dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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An Overview of Artificial Intelligence in the Detection of Suspicious Arabic Text on Social Media Platforms

An Overview of Artificial Intelligence in the Detection of S...

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2023 international conference on Artificial Intelligence Robotics, Signal and Image processing, AIRoSIP 2023

作者： Albalawi, Amal Yafooz, Wael M.S. University of Prince Mugrin Department of Computer Science College of Computer and Cyber Sciences Madinah Saudi Arabia Taibah University Department of Computer Science College of Computer Science and Engineering Madinah Saudi Arabia

ISBN: (纸本)9798350327472

This study examines the challenges of detecting and classifying offensive language in Arabic, with a focus on natural language processing (NLP) methods. Arabic's distinctive linguistic characteristics, such as its extensive morphology, present substantial obstacles to accurately classifying offensive content. This paper evaluates the approaches currently in use in this field and conducts a critical analysis of publicly accessible datasets frequently used to detect offensive language in Arabic. In addition to highlighting areas for further study and growth in the detection of Arabic offensive language, the aim is to offer insights into the state-of-the-art methodologies being used. In the analysis of public datasets, this paper evaluates their suitability for offensive language detection tasks in Arabic, considering factors such as dataset size, annotation quality, and representation of diverse offensive language categories. Furthermore, this paper investigates the existing methods utilized for detecting and classifying offensive language in Arabic, examining a range of NLP approaches used in the context of detecting Arabic offensive language. This includes machine learning algorithms, deep learning architectures, and linguistic aspects, all tailored to address the specific challenges associated with offensive language detection in the Arabic vernacular. Our investigation emphasizes the need for customized algorithms that take into account Arabic's unique linguistic characteristics. Future research should focus on developing advanced models that incorporate domain-specific knowledge, handle dialectal variations, and effectively capture the intricacies of Arabic morphology. © 2023 IEEE.

关键词： Tweets

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