The current model for LLM services involves receiving the service through cloud sources for all users. However, this solution is expected to have scalability problems. In this project, we intend to test the performance of the alternatives for offering LLM Services over Edge Computing.
Air computing is offering edge computing services over aerial vehicles such as UAVs.
We have developed a new simulator for Air Computing: AirCompSim.
This project involves adding energy consumption and constraints features to AirCompSim and testing the new simulator with energy aware resource allocation/trajectory computation for air computing.
In this project, you will develop a software for the recruitment process in the human resources domain. Recruitment refers to matching the CVs of candidates and the advertisements of companies. A mechanism for computing effective matching scores will be designed using large language models.
In this project, first you will survey the methods used for explaining the outcomes of large language models. Then you will implement these methods in an application area related to natural language processing.
The project aims to learn neuro-symbolic operators that are effective in planning sequence of actions in direct and inverse tasks. The data obtained from human action observations as well as robot’s own action executions, will be used to learn associations between low-level sensorimotor observations and high-level symbolic representations. The extracted symbols should be useful in planning, verification and inversion, therefore the extraction algorithms will be biased considering their
effectiveness for such tasks.