Project: Large language Model Based Chatbots and their Applications
Description
In recent years, large language models have revolutionized how machines understand and generate human-like text, offering profound implications for chatbot technology. This thesis proposes a deep exploration into the capabilities of these models within chatbot applications, aiming to enhance how they mimic human conversational patterns, manage contextual dialogue, and improve user interaction across various platforms.
Objectives
Related Work: Investigate current LLM architectures like GPT-4, BERT, and others to understand their underlying mechanisms and suitability for chatbot integration.
Performance Analysis: Evaluate the chatbots’ performance in real-world scenarios, measuring accuracy, responsiveness, and user satisfaction. Analyze the limitations and challenges faced by LLMs in generating coherent and contextually appropriate responses.
User Interaction and Engagement: Study user interaction patterns with LLM-based chatbots to identify key factors that enhance engagement and perceived intelligence. Explore modifications to model training and output processing to optimize conversational flow and relevance.
Ethical Considerations and Implications: Address potential ethical issues arising from chatbot interactions, including privacy concerns, misinformation, and dependency. Propose guidelines and best practices for ethical chatbot development and deployment.
Innovative Applications: Investigate novel applications of LLM-based chatbots beyond traditional roles, such as personalized learning assistants, automated content creators, or therapeutic aids. Evaluate the scalability of these applications and their potential social impact.
Expected Outcomes
This research is expected to yield a robust framework for developing LLM-based chatbots that can perform complex, multi-turn conversations with high accuracy and user satisfaction. It will also provide insights into the practical and ethical dimensions of deploying AI-driven conversational agents in sensitive fields.
By pushing the boundaries of what LLMs can achieve in interactive scenarios, this thesis aims to contribute to the broader field of artificial intelligence by demonstrating the potential of chatbots as more than simple query-response systems, positioning them as integral tools for information delivery and user support.
Details
- Supervisor
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Meng Fang
- Secondary supervisor
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Jiaxu Zhao
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