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The concept of Generative AI (GAI) is a kind of transformation technology which processes and analyses large datasets for predictive insights. The launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. The word embedding based data representation approach on a Machine Learning (ML) model provides a fixed data orientation in a dedicated way of implementation. In Generative AI (GAI’s), the implementation environment is diverse with a variety of attributes, epochs, and control parameters. Proper data transformation methods are indeed here to preserve proper semantics and operations on data. This paper put forward some fuzzy approaches to achieve enough data transformation in a GAI-based neural network environment.
Keywords:
Word embedding, Context, Conceptualization, Neural network, Generative AI
Cite Article:
"Transformations of Word to Vector Approaches in Generative AI ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a710-a716, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507078.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator