[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123346-en":3,"doc-seo-123346-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123346,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Leveraging Ethical Narratives to Enhance LLM-AutoML Generated Machine Learning Models","The growing popularity of generative AI and large language models (LLMs) has accelerated innovation while intensifying debate around plagiarism and intellectual property. A less discussed risk involves the reliability of code produced by these models, which can contain errors and reinforce poor programming practices. The paper proposes an integrated LLM-AutoML framework that uses AutoML for hyperparameter tuning and model selection, coupled with NLP/NLU to interpret chatbot prompts. A bias-filtering mechanism supports ethical AI, improving accountability. The methodology demonstrates practical implementation and high predictive accuracy, enabling more robust, reliable, and customizable ML algorithm generation from chatbot prompt features.","Expert Systems  \nORIGINAL ARTICLE  OPEN ACCESS   \nLeveraging Ethical Narratives to Enhance LLM-AutoML Generated Machine Learning Models  \nJordan Nelson1 | Michalis Pavlidis1 | Andrew Fish2 | Nikolaos Polatidis1  | Yannis Manolopoulos3   \n1School of Architecture, Technology and Engineering, University of Brighton, Brighton, UK | 2Department of Computer Science, University of Liverpool,  \nLiverpool, UK | 3Department of Informatics, University of Nicosia, Nicosia, Cyprus Correspondence: Nikolaos Polatidis ([n.polatidis@brighton.ac.uk](n.polatidis@brighton.ac.uk))  \nReceived: 1 January 2025 | Revised: 26 March 2025 | Accepted: 2 May 2025  \nFunding: This work was supported by the European Commission.  \nKeywords: artificial intelligence | AutoML | ethical AI | Google's Gemini | large language models | machine learning | natural language processing | OpenAI's ChatGPT  \nABSTRACT  \nThe growing popularity of generative AI and large language models (LLMs) has sparked innovation alongside debate, particularly around issues of plagiarism and intellectual property law. However, a less-discussed concern is the quality of code generated by these models, which often contains errors and encourages poor programming practices. This paper proposes a novel solution by integrating LLMs with automated machine learning (AutoML). By leveraging AutoML's strengths in hyperparameter tuning and model selection, we present a framework for generating robust and reliable machine learning (ML) algorithms. Our approach incorporates natural language processing (NLP) and natural language understanding (NLU) techniques to interpret chatbot prompts, enabling more accurate and customisable ML model generation through AutoML. To ensure ethical AI practices, we have also introduced a filtering mechanism to address potential biases and enhance accountability. The proposed methodology not only demonstrates practical implementation but also achieves high predictive accuracy, offering a viable solution to current challenges in LLM-based code generation. In summary, this paper introduces a new application of NLP and NLU to extract features from chatbot prompts, feeding them into an AutoML system to generate ML algorithms. This approach is framed within a rigorous ethical framework, addressing concerns of bias and accountability while enhancing the reliability of code generation.  \n1 | Introduction  \nThe rise of chatbots has been well documented. However, chatbots and generative AI have faced criticism, from potential IP law infringements to issues with the accuracy of data they provide to users (Intellectual Property in ChatGPT 2023, Fui-Hoon Nah et al. 2023) . Although LLM-based code generation offers promising advantages in accelerating productivity and automating tasks for businesses, a critical analysis of its successes and failures is essential. A publication examining the correctness of synthetic code identified several weaknesses and limitations in the evaluation power of the original test inputs from Human (Liu et al. 2023; Chen et al. 2021) . This publication highlighted  \nimprovements that could identify significant amounts of previously undetected code errors.  \nOne key hypothesis is to fundamentally reimagine the system for chatbot-generated code. A significant issue arises from the fact that code is often generated in a manner similar to text data, which can lead to suboptimal outcomes. Within chatbot training data, common programming practices, both good and bad, are frequently intermingled, making it challenging for the system to differentiate between them. This can result in the propagation of poor programming habits that are difficult to identify and rectify. To address these challenges, we propose two core concepts: First, the chatbot should be equipped with a more refined  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work ","cbCaijZFNPYW2Ppd","https://ap.wps.com/l/cbCaijZFNPYW2Ppd","pdf",1182216,1,16,"English","en",105,"# Introduction\n## Ethical and quality concerns in LLM-generated code\n## Reimagining chatbot-based code generation\n## AutoML as a foundation for robust ML models\n## Using NLP/NLU to interpret chatbot prompts","[{\"question\":\"What problem does the paper address in LLM-based code generation?\",\"answer\":\"It targets the quality issue where LLM-generated code may contain errors and encourage poor programming practices.\"},{\"question\":\"How does the proposed method combine LLMs with AutoML?\",\"answer\":\"It integrates LLM-driven prompt interpretation with AutoML’s hyperparameter tuning and model selection to produce robust, reliable ML algorithms.\"},{\"question\":\"What mechanism supports ethical AI in the framework?\",\"answer\":\"The approach includes a filtering mechanism designed to address potential biases and improve accountability.\"}]","Leveraging Ethical Narratives to Enhance LLM-AutoML Generated Machine Learning Models | PDF",1785816052,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"leveraging-ethical-narratives-to-enhance-llm-automl-generated-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/leveraging-ethical-narratives-to-enhance-llm-automl-generated-machine-learning-models/123346/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in LLM-based code generation?","Question",{"text":75,"@type":76},"It targets the quality issue where LLM-generated code may contain errors and encourage poor programming practices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method combine LLMs with AutoML?",{"text":80,"@type":76},"It integrates LLM-driven prompt interpretation with AutoML’s hyperparameter tuning and model selection to produce robust, reliable ML algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"What mechanism supports ethical AI in the framework?",{"text":84,"@type":76},"The approach includes a filtering mechanism designed to address potential biases and improve accountability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":29,"slug":116},7,"Healthcare","healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]