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Detecting Multiple Mental Health Disorders with Large language Models 28

Detecting Multiple Mental Health Disorders with Large Langua...

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28th international conference on Information Visualisation (IV)

作者： Nanda, Mehul Inkpen, Diana Dargel, Aroldo Univ Ottawa Ottawa ON Canada Ottawa Hosp Ottawa ON Canada

ISBN: (纸本)9798350380170;9798350380163

Artificial Intelligence systems have become useful in many sectors, including healthcare. In this paper, we focus on detecting signs of mental health disorders from social media texts. This can be used to direct patients to consult a healthcare professional, while on waiting lists, or for post-monitoring. The performance of the current algorithms for detecting multiple disorders is limited. We propose a new method to increase performance by including target-domain knowledge for each type of mental health disorder (for nine disorders). We used this information to select the best training examples to include in our carefully engineered prompts for performing few-shot learning. We show that the results improved when compared to zero-shot learning based on Large language Models and when compared to state-of-the-art results on the same test set.

关键词： AI and Healthcare Predictions natural language processing Transformers BERT Large language Models Domain knowledge

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Learning from Mistakes: A Comprehensive Review of knowledge Editing for Large language Models 8

Learning from Mistakes: A Comprehensive Review of Knowledge ...

引用

8th international conference on Smart Internet of Things

作者： Li, Yanhong Fan, Chunling Huang, Mingqing Li, Chengming Shenzhen MSU BIT Univ Artificial Intelligence Res Inst Shenzhen Peoples R China Harbin Inst Technol Shenzhen Sch Sci Shenzhen Peoples R China Shenzhen Polytech Univ Sch Elect & Commun Engn Shenzhen Peoples R China Shenzhen Inst Informat Technol Sch Comp & Software Shenzhen Peoples R China

ISBN: (纸本)9798350366457;9798350366440

In recent years, there has been a growing recognition that large language models like GPT-4 have the capability to store vast amounts of knowledge and possess extremely powerful abilities. However, these models are not immune to making mistakes. The knowledge stored within these models may be erroneous or outdated, leading to incorrect outputs. Consequently, it is imperative to implement methods to edit the knowledge within the models. In this paper, we primarily summarize and organize several key knowledge editing methods that have emerged in recent years. By drawing an analogy to the human process of correcting mistakes, we categorize these methods into three types: rote memorization, learning from examples, and thoroughly understanding. In addition, we briefly discuss the potential positive impact of researches on the knowledge mechanisms of large models on knowledge editing. By summarizing the existing researches on the knowledge of large models, we also briefly explore the role of fully utilizing knowledge mechanisms and resolving knowledge conflicts in enhancing the effectiveness of knowledge editing.

关键词： Large language Model nature language processing knowledge editing

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ISR-LLM: Iterative Self-Refined Large language Model for Long-Horizon Sequential Task Planning

ISR-LLM: Iterative Self-Refined Large Language Model for Lon...

引用

ieee international conference on Robotics and Automation (ICRA)

作者： Zhou, Zhehua Song, Jiayang Yao, Kunpeng Shu, Zhan Ma, Lei Univ Alberta Edmonton AB Canada Swiss Fed Inst Technol Lausanne EPFL Lausanne Switzerland Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350384581;9798350384574

Motivated by the substantial achievements of Large language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges in robotics. LLMs are advantageous in offering the potential to enhance the generalizability as task-agnostic planners and facilitate flexible interaction between human instructors and planning systems. However, task plans generated by LLMs often lack feasibility and correctness. To address this challenge, we introduce ISR-LLM, a novel framework that improves LLM-based planning through an iterative self-refinement process. The framework operates through three sequential steps: preprocessing, planning, and iterative self-refinement. During preprocessing, an LLM translator is employed to convert natural language input into a Planning Domain Definition language (PDDL) formulation. In the planning phase, an LLM planner formulates an initial plan, which is then assessed and refined in the iterative self-refinement step by a validator. We examine the performance of ISR-LLM across three distinct planning domains. Our experimental results show that ISR-LLM is able to achieve markedly higher success rates in sequential task planning compared to state-of-the-art LLM-based planners. Moreover, it also preserves the broad applicability and generalizability of working with natural language instructions.

关键词： natural language processing systems

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Transforming Adolescent Healthcare in Rwanda: Sustainable and Scalable Features in Digital Health

Transforming Adolescent Healthcare in Rwanda: Sustainable an...

引用

ieee-EMBS international conference on Biomedical and Health Informatics (BHI)

作者： Dasari, Tejaswi Samrat Satish, Pratima Kayitesi, Laetitia Mbuvi, Mary Dias, Russell Ngamije, Fabiola Ishimwe Kwesiga, Joseph On, Robert Woodbridge, Diane Myung-Kyung Univ San Francisco San Francisco CA 94117 USA Univ Calif Berkeley Berkeley CA 94720 USA YLabs Berkeley CA USA Agcy Fund Albuquerque NM USA

ISBN: (纸本)9798350310504

CyberRwanda is a digital health intervention designed to increase knowledge of family planning and reproductive health (FP/RH) and access to youth-friendly services in Rwanda. Previous studies showed high acceptability and feasibility through user surveys, success in combating social stigma, and an effective educational impact on adolescents. In order to further enhance user experience and overcome resource limitations, the authors designed and developed new features, including an integrated dashboard and chatbot, leveraging data engineering and natural language processing. The experiments showed that new features substantially reduce the time required to access information for user groups, potentially leading to an improved user experience.

关键词： Big Data applications Data engineering natural language processing Public healthcare Smart healthcare

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Model Generation with LLMs: From Requirements to UML Sequence Diagrams 32

Model Generation with LLMs: From Requirements to UML Sequenc...

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32nd ieee international Requirements engineering conference (RE)

作者： Ferrari, Alessio Abualhaija, Sallam Arora, Chetan Consiglio Nazl Ric CNR Florence Italy SnT Univ Luxembourg Luxembourg Luxembourg Monash Univ Clayton Vic Australia

ISBN: (纸本)9798350395525;9798350395518

Complementing natural language (NL) requirements with graphical models can improve stakeholders' communication and provide directions for system design. However, creating models from requirements involves manual effort. The advent of generative large language models (LLMs), ChatGPT being a notable example, offers promising avenues for automated assistance in model generation. This paper investigates the capability of ChatGPT to generate a specific type of model, i.e., UML sequence diagrams, from NL requirements. We conduct a qualitative study in which we examine the sequence diagrams generated by ChatGPT for 28 requirements documents of various types and from different domains. Observations from the analysis of the generated diagrams have systematically been captured through evaluation logs, and categorized through thematic analysis. Our results indicate that, although the models generally conform to the standard and exhibit a reasonable level of understandability, their completeness and correctness with respect to the specified requirements often present challenges. This issue is particularly pronounced in the presence of requirements smells, such as ambiguity and inconsistency. The insights derived from this study can influence the practical utilization of LLMs in the RE process, and open the door to novel RE-specific prompting strategies targeting effective model generation.

关键词： natural language processing (NLP) Large language Models (LLMs) Prompt engineering ChatGPT Model Generation Sequence Diagrams

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Exploring language Models: A Comprehensive Survey and Analysis

Exploring Language Models: A Comprehensive Survey and Analys...

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

作者： Singh, Aditi Department of Computer Science Cleveland State University Ohio United States

ISBN: (纸本)9798350305708

The domain of natural language processing (NLP) has witnessed significant advancements with the arrival of large-scale pre-trained language models to revolutionize NLP research and achieve state-of-the-art performance across various tasks. They have propelled the development of sophisticated NLP applications and deepened our understanding of artificial intelligence. However, the growing size and complexity of these models have given rise to new challenges and limitations. Concerns related to model bias, interpretability, data privacy, and environmental impact have become prominent. This paper explores the impact and potential of large language models in NLP, highlighting the advancements made and the challenges that need to be addressed. © 2023 ieee.

关键词： ALBERT artificial intelligence BERT BLOOM GLM GPT GPT-3 large-scale pre-trained language models LLAMA natural language processing RoBERTa state-of-the-art T5 XLNeT

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A Study on Monitoring Water Pollution Events Based on natural language processing and Social Media 5

A Study on Monitoring Water Pollution Events Based on Natura...

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

作者： She, Jian Cent Southern Safety & Environm Technol Inst Co L Wuhan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

With the rapid development of social and economic development, China's environmental development is also extremely rapid, but there is still the phenomenon of sudden water pollution of the environment, which not only affects the cleanliness of the river waters, but also directly affects the residents of the normal life of water. Under the big data environment, a large amount of relevant information generated by news platforms or social media platforms when various sudden events occur can provide data support for event detection and information acquisition. In this paper, we use the named entity recognition technology in natural language processing to build a BERT-BiLSTM-CRF model for water pollution event monitoring using tweets from social media platforms as data sources. The experimental results show that the model extracts F1 values up to 0.9119, and the extraction results can provide additional data support for the discovery and monitoring of water pollution events around the world.

关键词： component Water pollution incidents natural language processing Deep learning models Social media

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Providing a natural language processing App for language Teachers 26th

Providing a Natural Language Processing App for Language Tea...

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26th international conference on Interactive Collaborative Learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Posekany, Alexandra Dolezal, Dominik TU Vienna Univ Technol Vienna Austria TGM Vienna Inst Technol Vienna Austria Univ Vienna Vienna Austria

ISBN: (纸本)9783031519789;9783031519796

natural language processing (NLP) is a common application for Artificial Intelligence. The goal is to provide language teachers with a simple to apply tool for topic model analyses to integrate into their classroom. The project also involves project based learning for students programming the actual AI web application. The original notion is to provide language teacher with AI methodology without requiring any technical knowledge in AI or any programming skills. natural language processing provides various tools for word frequencies, but also topic modelling, allowing to track the relevance of topics over time in the media or in the literature. In collaboration with University of Technology linguistics, we intend to provide a corpus of classical English and German literature, as well as the option of uploading your own corpus which can be obtained from webscraping or other sources. A team of students of the vocational high school TGM Wien specialised in IT and Software Development is working on the design of the interactive GUI for this NLP application, learning in this way the methods of natural language processing and Artrificial Intelligence in a project based setting. For this the statistical programming language R is utilized which already provides packages with implementation for natural language processing and in addition the shiny package which allows to develop interactie web apps without additional web and app programming. A team of teachers supervises and supports the students during the development process, providing expertise in AI and NLP, in web and app programming, as well as server management. Two intended outcomes exist. Ont the one hand, we want our students to learn natural language processing first hand through development of this application. On the other hand, we intend to obtain an interactive AI tool which can assist language teachers and their students on the long term in the classroom. In times of GPT3 and GPT4 dominating the media and per

关键词： Artificial intelligence natural language processing Interactive web application Project based learning Software development

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An Industrial Short Text Classification Method Based on Large language Model and knowledge Base

An Industrial Short Text Classification Method Based on Larg...

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international Joint conference on Neural Networks (IJCNN)

作者： Yin, Haoran Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Text classification is a common task in natural language processing tasks. However, in reality, such as industrial datasets, there are numerous short texts that are ignored by existing models, in which important information is inevitably missed due to the short word count. Recent studies tend to enrich the features in short text classification by introducing conceptual information. However, these simple conceptual information is also relatively sparse, with limited feature augment. In this paper, we propose Input Embedding, knowledge Base Retrieval, Large language Model Text Augmentation, and Text Encoding for the task of short text classification (IKLT). The four modules utilize the current well-performing forms of Large language Model and Retrieval Augmentation to further enrich the feature information and have interpretability. Moreover, in addition to some public datasets we establish an industrial short text dataset for comparison experiments. The experimental results show that our proposed framework for short text classification achieves improved results on all five datasets.

关键词： short text classification deep learning LLM knowledge base attention

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Supporting the construction of mystery novel knowledge graphs using BERT 17

Supporting the construction of mystery novel knowledge graph...

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17th ieee international conference on Semantic Computing (ICSC)

作者： Hasegawa, Kazuma Fujii, Akihiro Hosei Univ Dept Appl Informat Fac Sci & Engn Tokyo Japan Hosei Univ Tokyo Japan

ISBN: (纸本)9781665482639

In the knowledge Graph Reasoning Challenge, the plot of a mystery novel is converted into a knowledge graph. Building the graph requires the work of choosing the parts to put into the knowledge graph. This research aims to automate this task. Specifically, BERT, a natural language processing model, is used to classify with either summary or non-summary, with the results proposed as the parts to put into the knowledge graph. We established a binary classification model and verified its accuracy. Its was found to have an F-value of 0.59. Given the likelihood of improving the accuracy, it is thought that organizing the dataset could have a significant impact. This is to be pursued as a next step.

关键词： BERT classification natural language processing

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