About this Research Topic
Led by our distinguished Associate Editors, Profs. Chang-Tien Lu, Xiangjie Kong, Xiuzhen Jenny Zhang, Hariharan Shanmugasundaram & Shou-De Lin, this project explores the intricate landscape of large language models and their application in the realm of data mining. Through mining voluminous datasets, language models scour for hidden patterns, correlations, and insights to provide a comprehensive picture of the data landscape.
By ingesting and analyzing bodies of text, these models empower predictive text generation, linguistic structure recognition, and relevance detection. They apply these proficiencies in various applications - determining sentiment in customer reviews, refining recommendations on online platforms, and powering humanness in chatbots, amongst a range of other areas. By utilizing the mechanisms of deep learning and natural language processing, these models transform massive, unstructured data into meaningful, valuable knowledge, decoding language, and unlocking insights on a scale that would not be humanly possible.
Yet, the field is not without its limitations. Large language models grapple with demands for advanced computational resources, intricacies of linguistic nuances, and the need for meticulous design to ensure unbiased and ethical interpretations.
This Research Topic investigates the potential, applications, and ever-evolving methodologies of large language models, shedding light on the allure and challenges of automated linguistic proficiency in the context of data mining. Topics include but are not limited to:
- Data Privacy in Data Mining
- Data Ownership and Intellectual Property
- Algorithmic Bias and Discrimination
- Ethical Bias and Mitigation in Data Mining
- Generative AI for Data Mining Applications
- Data Science and Data intelligence
- Machine Learning for Predictive Data Analysis
By ingesting and analyzing bodies of text, these models empower predictive text generation, linguistic structure recognition, and relevance detection. They apply these proficiencies in various applications - determining sentiment in customer reviews, refining recommendations on online platforms, and powering humanness in chatbots, amongst a range of other areas. By utilizing the mechanisms of deep learning and natural language processing, these models transform massive, unstructured data into meaningful, valuable knowledge, decoding language, and unlocking insights on a scale that would not be humanly possible.
Yet, the field is not without its limitations. Large language models grapple with demands for advanced computational resources, intricacies of linguistic nuances, and the need for meticulous design to ensure unbiased and ethical interpretations.
This Research Topic investigates the potential, applications, and ever-evolving methodologies of large language models, shedding light on the allure and challenges of automated linguistic proficiency in the context of data mining. Topics include but are not limited to:
- Data Privacy in Data Mining
- Data Ownership and Intellectual Property
- Algorithmic Bias and Discrimination
- Ethical Bias and Mitigation in Data Mining
- Generative AI for Data Mining Applications
- Data Science and Data intelligence
- Machine Learning for Predictive Data Analysis
Keywords: Large Language Models, Data Mining, Linguistic Proficiency, Datasets, Knowledge Discovery
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
