Rationality Check! Benchmarking the Rationality of Large Language Models
This work addresses the concern about LLM rationality for developers and users, offering a foundational tool, though it is incremental as it builds on existing evaluation methods.
The authors tackled the problem of assessing whether large language models (LLMs) think and behave like humans by proposing the first benchmark for evaluating their omnibus rationality across domains, providing an easy-to-use toolkit and extensive experimental results to analyze convergence and divergence from human rationality.
Large language models (LLMs), a recent advance in deep learning and machine intelligence, have manifested astonishing capacities, now considered among the most promising for artificial general intelligence. With human-like capabilities, LLMs have been used to simulate humans and serve as AI assistants across many applications. As a result, great concern has arisen about whether and under what circumstances LLMs think and behave like real human agents. Rationality is among the most important concepts in assessing human behavior, both in thinking (i.e., theoretical rationality) and in taking action (i.e., practical rationality). In this work, we propose the first benchmark for evaluating the omnibus rationality of LLMs, covering a wide range of domains and LLMs. The benchmark includes an easy-to-use toolkit, extensive experimental results, and analysis that illuminates where LLMs converge and diverge from idealized human rationality. We believe the benchmark can serve as a foundational tool for both developers and users of LLMs.