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Abstract Recent strides in generative artificial intelligence, particularly large language models, have been propelled by foundation models – learning algorithms trained on extensive and diverse datasets encompassing various subjects. This technology, inspired by the complexity of the human brain, unveils a new frontier in generative Artificial Intelligence (AI), showing its potential in creativity by generating innovative content based on absorbed data and user prompts. It is forecasted that the conversational AI and virtual assistant segment is experiencing the highest growth rate within the contact center industry, projected to fuel a 24% increase in the market during 2024. In spite of all remarkable performances, the incipient stage of generative AI calls for a careful consideration, as technological and ethical challenges demand attention and awareness. This research delves into the base principle which empowers users to build personalized chatbots trained on your data. This stand-alone footprint can further exemplify the transformative potential of generative artificial intelligence, extending its reach beyond professionals to individuals and tremendously remodeling the landscape of chatbots. Text generation lies at the intersection of computational linguistics and artificial intelligence, forming a specialized area within natural language processing. It implies a thorough procedure where a model is trained to be able to recognize and interpret the context of specific input data, subsequently generating text that pertains to the input’s subject matter. We have identified gap areas that require in-depth research. For instance, a broader number of papers relies solely on architecture optimization, performance comparison or application-specific studies. Therefore, this paper gives a bird’s eye view of the effective algorithm flow of a traditional generative model, using Long Short-Term Memory networks – part of the recurrent neural networks part family. The purpose of the current study focuses to enrich the existing body of knowledge on how a response generation-based model operates, therefore paving the way for chatbots development and deployment.
Dumitru et al. (Sat,) studied this question.