Capability Evolution
Capability evolution in artificial intelligence focuses on understanding and enhancing the abilities of various AI models, particularly large language models (LLMs), across diverse tasks. Current research emphasizes evaluating these capabilities through novel benchmarks and frameworks, often analyzing model performance under incomplete information or with limited data, and exploring the role of factors like data quality and model architecture (e.g., transformers, state space models). This research is crucial for responsible AI development, informing the creation of more robust and reliable systems with applications ranging from robotics and software engineering to education and scientific research.
Papers
Exploring How Non-Prehensile Manipulation Expands Capability in Robots Experiencing Multi-Joint Failure
Gilberto Briscoe-Martinez, Anuj Pasricha, Ava Abderezaei, Santosh Chaganti, Sarath Chandra Vajrala, Sri Kanth Popuri, Alessandro Roncone
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation
Bhargav Shandilya, Alexis Palmer