Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models
Kevin Leyton-Brown, Yoav Shoham·June 16, 2024
Summary
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.
Introduction
Background
Emergence of large language models and limitations in understanding
The Turing Test as a historical reference point
Objective
To propose a practical framework for evaluating AI understanding
Address challenges in complex domains and nontrivial subjects
Provide guidelines for future AI development
Method
Data Collection
Random sampling of questions and tasks
Involvement of diverse subject areas
Data Preprocessing
Selection of appropriate question types for evaluation
Handling ambiguity and context in questions
Evaluation Metrics
Scope: measuring understanding across varying levels of complexity
Competence: observed performance in specific domains
Avoidance of ridiculous responses: probability-based assessment
Confidence and Certainty
-承认不确定性在复杂领域的困难
Probabilistic confidence bounds for practical evaluation
Challenges of Infinite Scopes
Addressing the scalability issue in evaluating understanding
The need for adaptability in AI systems
Differentiating Observed Competence and Internal Inspection
Measuring external behavior vs. analyzing system internals
The role of average scores in assessing understanding
Proposed Test
Average scores as a benchmark
Low probability of providing ridiculous answers as a criterion
Framework Structure
Structuring the debate on AI understanding
Current limitations and future directions
Practical Guidelines
Recommendations for improving AI agents' performance
Iterative development and testing strategies
Conclusion
Summary of key contributions
Implications for the AI research community
Future research directions in evaluating AI understanding
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features