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The work examines ESG concepts and large language model foundations, then develops fine-tuning experiments, classifier training, and a comparative evaluation framework using performance metrics. Implementation, data collection and preprocessing, refinement cycles, and ethical considerations support the research design, hypotheses, and research objectives.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/evaluating-the-performance-of-state-of-the-art-esg-domain-specific-pre-trained-large-language-models-in-text-classification-against-existing-models-and-traditional-machine-learning-techniques-master-of-science-dissertation/128820/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/evaluating-the-performance-of-state-of-the-art-esg-domain-specific-pre-trained-large-language-models-in-text-classification-against-existing-models-and-traditional-machine-learning-techniques-master-of-science-dissertation/128820.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-19","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of the dissertation?","Question",{"text":112,"@type":113},"The dissertation evaluates how well ESG domain-specific pre-trained large language models perform for text classification compared with existing models and traditional machine learning techniques.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which model families and approaches are reviewed and compared?",{"text":117,"@type":113},"The literature review covers ESG, LLMs, BERT, LLaMA2, classical ML methods including SVM and XGBoost, and LLM fine-tuning, including QLoRA.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the research carried out and evaluated?",{"text":121,"@type":113},"The methodology includes fine-tuning experiments, classifier training, data collection and preprocessing, and performance evaluation metrics, followed by refinement and re-evaluation through iterative cycles.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},128820,1786003691,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Department of Computer Science  \nEVALUATING THE PERFORMANCE OF STATE-OF-THEART ESG DOMAIN-SPECIFIC PRE-TRAINED LARGE LANGUAGE MODELS IN TEXT CLASSIFICATION AGAINST EXISTING MODELS AND TRADITIONAL MACHINE LEARNING TECHNIQUES  \nModule Code: COM00151M  \nModule Name: Independent Research Project  \nby Tin Yuet CHUNG  \nJune 2024  \nsupervised by Dr. Majid Latifi  \nword count: 9395  \nA dissertation submitted in partial fulfilment of the requirement for  \nthe degree of  \nMaster of Science in Computer Science with Artificial Intelligence  \nAcknowledgement  \nFirst of all, I would like to thank Dr. Majid Latifi for his supervision and help throughout the duration of the project. In the last few months, Majid has provided me the direction of research and has given insightful comments. I also want to thank Tobias Schimanski and his research group who contributed to the ESG annotated text data.  \nSecondly, I would like to thank my friends in Hong Kong and Canada.  \nLast but not least, I would like to thank my family for supporting me in Canada.  \nTable of Contents  \nAcknowledgement ................................................................................................................. 0  \nList of Figures ........................................................................................................................ 3  \nLists of Tables ........................................................................................................................ 3  \nTable of Abbreviations ............................................................................................................ 4  \nExecutive Summary ............................................................................................................... 5  \n1- Introduction ....................................................................................................................... 7  \n1.1 Motivation and Background ........................................................................................... 7  \n1.2 Research Aim ............................................................................................................... 7  \n1.3 Research Focus ............................................................................................................ 8  \n1.4 Research Questions/Hypotheses .................................................................................. 8  \n1.5 Research Objectives ..................................................................................................... 8  \n1.6 Structure of the dissertation .......................................................................................... 8  \n2-Literature Review ............................................................................................................. 10  \n2.1 Environment, Social, and Governance (ESG) ................................................................ 10  \n2.2 Large Language Model (LLM) ....................................................................................... 10  \n2.3 BERT (Bidirectional Encoder Representations from Transformers) ................................ 11  \n2.4 LLaMA2 (Large Language Model Meta AI 2) ................................................................... 12  \n2.5 Classical Machine Learning (ML), Support Vector Machine (SVM) and XGBoost............. 13  \n2.6 LLM Fine-Tuning.......................................................................................................... 14  \n2.6.1 What is LLM Fine-Tuning? ..................................................................................... 14  \n2.6.2 Advantages and real-world applications of fine-tuned models ............................... 15  \n2.6.3 LLM Fine-Tuning Techniques ................................................................................. 15  \n2.6.4 Qlora.................................................................................................................... 16  \n2.7 Research Gap ................","cbCainOtzjbu7R2m","https://ap.wps.com/l/cbCainOtzjbu7R2m","pdf",1745903,58,"English","# Executive Summary\n# Introduction\n## Motivation and Background\n## Research Aim\n## Research Focus\n## Research Questions/Hypotheses\n## Research Objectives\n## Structure of the dissertation\n# Literature Review\n## Environment, Social, and Governance (ESG)\n## Large Language Model (LLM)\n## BERT (Bidirectional Encoder Representations from Transformers)\n## LLaMA2 (Large Language Model Meta AI 2)\n## Classical Machine Learning (ML), Support Vector Machine (SVM) and XGBoost\n## LLM Fine-Tuning\n## Research Gap\n# Methodologies and Method\n## Research Philosophy\n## Research Method/Methodology\n## Research Design\n## Implementation\n## Ethical Considerations","[{\"question\":\"What is the main objective of the dissertation?\",\"answer\":\"The dissertation evaluates how well ESG domain-specific pre-trained large language models perform for text classification compared with existing models and traditional machine learning techniques.\"},{\"question\":\"Which model families and approaches are reviewed and compared?\",\"answer\":\"The literature review covers ESG, LLMs, BERT, LLaMA2, classical ML methods including SVM and XGBoost, and LLM fine-tuning, including QLoRA.\"},{\"question\":\"How is the research carried out and evaluated?\",\"answer\":\"The methodology includes fine-tuning experiments, classifier training, data collection and preprocessing, and performance evaluation metrics, followed by refinement and re-evaluation through iterative cycles.\"}]","Evaluating the Performance of State-of-the-Art ESG Domain-Specific Pre-Trained Large Language Models in Text Classification Against Existing Models and Traditional Machine Learning Techniques - Master of Science Dissertation | PDF",146]