Interactive Text Generation Using GPT-2 with Gradio: An Approach to Accessible NLP Applications

Abstract

Natural language processing was revolutionized by the development of large language models (LLMs) such as gpt-2, and similar technologies that can be used for chatbots, writing assistance, and artificial intelligence in education, among numerous other applications. These powerful models nevertheless come with accessibility and usability barriers for specific nontechnical users, limiting their broader usage. The present study proposes to fill this gap by incorporating GPT-2 with Gradio, an open-source platform for deploying machine learning models via easy-to-use web interfaces. We present a novel approach of using GPT-2 for interactive text generation, taking advantage of GPT-2's ability to generate continuous language in a very natural, user-friendly way. The system accommodates both technical and non-technical users and requires very little configuration or coding knowledge in order to generate text easily. The architecture of the system, how users interact with it, and important choices in its implementation are described in this paper. Use cases including educational assistance, writing research articles, and generating creative content are analyzed to show their utility and challenges including redundancy in outputs, limitations in computer resources, and scalability. Possible remedies, such as changes to the decoding procedure and the model training process, are proposed. It is part of the effort to get AI to be more user-friendly and interactive, to close the gap between sophisticated NLP and a user- friendly interface. Also, it lays the foundation for a continued line of work on interactive, explainable, and personalized systems of language generation.

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