Succinct Representations for Concepts
This addresses the issue of model inaccuracy in AI systems, though it appears incremental as it builds on existing foundation models.
The paper tackles the problem of foundation models producing false answers by introducing succinct concept representations based on category theory, which yields a new learning algorithm that can provably and accurately learn complex concepts or fix misconceptions.
Foundation models like chatGPT have demonstrated remarkable performance on various tasks. However, for many questions, they may produce false answers that look accurate. How do we train the model to precisely understand the concepts? In this paper, we introduce succinct representations of concepts based on category theory. Such representation yields concept-wise invariance properties under various tasks, resulting a new learning algorithm that can provably and accurately learn complex concepts or fix misconceptions. Moreover, by recursively expanding the succinct representations, one can generate a hierarchical decomposition, and manually verify the concept by individually examining each part inside the decomposition.