Evaluation of Neural-Network and Large-Language Model Approaches for Generating Instructions for Animations

Abstract

Conversational agents are used more and more in customer service, health care, for educational purposes. The fundamental problems of conversational agents are many, including limitations in interpretation of complex queries and lack of emotional intelligence. Despite this, there are distinct advantages of conversational agents, such as efficient data analysis, reduction of operational costs and aid in interactive learning for personalized teaching. The most significant challenge this project aims to undertake is to generate realistic and complex animations in the context of interactive learning with a real-time constraint. The investigation includes how to select machine learning tools and models to aid in the advancement of animation generation, by using both Large-Language Models and purposely constructed Neural Networks. While Large-Language Models are convenient when used in straightforward conditions, Neural Networks are more dependable in an operative application thanks to their consistent format, adaptability and specifically developed purpose.