To enhance the integration of Large Language Models (LLMs) within UniHSI and make the framework more scalable and seamless, the following strategies can be implemented:
Fine-tuning and Transfer Learning: Continuously fine-tuning the LLMs on interaction data generated by the framework can improve their understanding of complex interactions. Additionally, leveraging transfer learning techniques to adapt pre-trained LLMs to the specific domain of human-scene interactions can enhance their performance.
Multi-Modal Inputs: Integrating multi-modal inputs, such as visual data from the environment, can provide additional context for the LLMs to generate more accurate and context-aware interaction plans. This can improve the overall performance and scalability of the framework.
Interactive Learning: Implementing interactive learning mechanisms where the LLMs can receive feedback from the environment or users during the planning process can help refine the generated plans in real-time. This interactive feedback loop can improve the adaptability and scalability of the framework.
Efficient Prompt Engineering: Refining the prompt engineering techniques to provide more structured and informative prompts to the LLMs can enhance their ability to generate coherent and realistic interaction plans. This can streamline the planning process and make it more efficient.
By implementing these strategies, the integration of LLMs within UniHSI can be further improved, making the framework more scalable, adaptable, and seamless in generating diverse and complex interaction plans.