Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models

The paper proposes a pragmatic framework for assessing an AI agent's understanding through question-answering, considering factors like scope, competence, and avoiding ridiculous responses. It suggests using random sampling and probabilistic confidence bounds for practical evaluation, acknowledging that certainty is difficult in complex domains. The authors argue that current large language models lack understanding of nontrivial subjects but provide a tool for future development. The paper defines understanding mathematically, drawing from the Turing Test, and discusses the challenges of evaluating understanding in infinite scopes. It differentiates between observed competence and internal system inspection, and proposes a test based on average scores and a low probability of ridiculous answers. The framework aims to structure the ongoing debate on AI understanding and offers practical guidelines for improving AI agents' performance.