Model Capability

Model capability research focuses on understanding and evaluating the strengths and limitations of machine learning models, particularly large language models (LLMs), across diverse tasks. Current efforts concentrate on developing robust evaluation frameworks, including standardized benchmarks and qualitative assessments, and exploring techniques to enhance model expressivity through architectural innovations like novel attention mechanisms. This research is crucial for building trustworthy and reliable AI systems, informing responsible development and deployment across various applications, from robotics to healthcare.