Minimization of Boolean Complexity in In-Context Concept Learning
This work addresses the problem of understanding learning biases in LLMs for researchers in AI and cognitive science, but it is incremental as it applies existing human concept learning insights to LLMs.
The study investigated factors affecting in-context learning success in LLMs by testing them on concept learning tasks, finding that performance strongly correlates with Boolean complexity, indicating a bias for simplicity similar to humans.
What factors contribute to the relative success and corresponding difficulties of in-context learning for Large Language Models (LLMs)? Drawing on insights from the literature on human concept learning, we test LLMs on carefully designed concept learning tasks, and show that task performance highly correlates with the Boolean complexity of the concept. This suggests that in-context learning exhibits a learning bias for simplicity in a way similar to humans.