International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
Testing Frameworks for AI-Powered Group Chats: A study on GPT-2, Hugging Face Transformers, and External Function Integration Deepika Panchal1, Prof. Vinod Kumar2, Prof. Aditya Kumar3 1M.Tech (Computer Science & Engineering) II Year, H.R. Institute of Technology, Ghaziabad, India 2Electronics & Communication Engineering, H.R. Institute of Technology, Ghaziabad, India
3Computer Science & Engineering, H.R. Institute of Technology, Ghaziabad, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The proposed paper, "Testing Frameworks for
to push the boundaries of conversational AI and simulate complex external functionalities.
AI-Powered Group Chats: A Study on GPT-2, Hugging Face Transformers, and External Function Integration," presents an advanced framework for improving AI-driven group chat systems. We integrate OpenAI’s GPT-2 and Hugging Face Transformers to leverage their superior text generation and contextual understanding capabilities. The research emphasizes the enhancement of conversational depth and interactivity through these models. Additionally, we explore the incorporation of simulated external functionalities, such as API interactions and real-time data processing, to extend the operational scope of AI agents. This approach aims to provide more dynamic, contextually relevant, and responsive interactions within group chat environments. To ensure the reliability and effectiveness of our proposed framework, we conducted rigorous software testing, focusing on the accuracy, scalability, and performance of AI interactions within the group chat system. Our findings demonstrate how combining these cutting-edge NLP technologies with external function simulations can create highly sophisticated, user-centric AI chat systems.
Language models have transformed the way we interact with technology, enabling more intuitive and natural conversations with chatbots, virtual assistants, and other AI-driven systems. The advent of models like GPT-2 has brought significant improvements in generating coherent, contextually relevant text. GPT-2, a state-of-the-art model developed by OpenAI, is known for its extensive training on diverse datasets, which equips it with the ability to generate human-like responses across various contexts. Similarly, the Hugging Face Transformers framework has revolutionized the field by providing accessible tools and pre-trained models that facilitate the development of advanced natural language processing (NLP) applications. This framework supports a wide array of transformer models, including GPT-2, BERT, and others, which are integral to building sophisticated conversational agents. Despite these advancements, integrating these technologies into cohesive systems that can handle complex interactions and simulate external functionalities remains a challenging task. Existing chat systems often struggle with limitations in their conversational capabilities and lack the ability to interface with external systems effectively. The objective of this research is to address these challenges by combining the strengths of GPT-2 and Hugging Face Transformers with external function simulations, thereby enhancing the overall performance and versatility of AI-driven group chat systems.
Key Words: AI Group Chat System, GPT-2, Hugging Face Transformers, External Function Simulation, Next-Gen AI Interactions, Testing, Conversational AI, Text Generation, Model Integration, Simulated External Functions, Chatbot Functionality, AI Agents, RAG (Retrieve and Generate), Natural Language Processing (NLP), Machine Learning Models, Transformer Models, Chat Simulation, Intelligent Agents, OpenAI Int
1.INTRODUCTION
The primary objective of this research is to develop an advanced AI group chat system that leverages the capabilities of GPT-2 and Hugging Face Transformers to improve conversational quality and simulate external functions. Specifically, this study aims to:
In the rapidly evolving landscape of artificial intelligence (AI), the ability to understand and generate human-like text has become a cornerstone of many modern applications. Language models, particularly those based on deep learning techniques, play a pivotal role in advancing this field. Among these, models such as OpenAI’s GPT-2 and frameworks like Hugging Face Transformers stand out due to their remarkable capabilities in natural language understanding and generation. This research paper explores the integration of these technologies to enhance group chat systems, aiming
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1. Enhance Conversational Capabilities: Utilize GPT-2’s advanced language generation abilities to create more coherent, context-aware, and engaging dialogues within group chat environments.
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