From Experiments to Discovery: A Principled Approach to Measuring How Well LLMs Do Science

Kanishk Gandhi, Michael Y. Li, Lyle Goodyear, Agam Bhatia, Ying Li, Aditi Bhaskar, Mohammed Zaman, Noah Goodman

Keywords: automated model discovery, experimental design, oed, probabilistic modeling, Box's Loop, large language models

TL;DR: We introduce BoxingGym, a benchmark to evaluate an LLM's ability to propose, test, and revise scientific theories in a principled way.

Abstract:

Understanding the world and explaining it with scientific theories is a central aspiration of artificial intelligence research.
Proposing theories, designing experiments to test them, and then revising them based on data are key to scientific discovery.
Despite the promise of LLM-based scientific agents, no benchmarks systematically test their ability to propose scientific models, collect experimental data, and revise them in light of new data. We introduce BoxingGym, a benchmark with 10 environments for evaluating experimental design (e.g. collecting data to test a scientific theory) and model discovery (e.g. proposing and revising scientific theories). To enable quantitative and principled evaluation, we implement each environment as a generative probabilistic model with which a scientific agent can run interactive experiments. These probabilistic models are drawn from various real-world scientific domains ranging from psychology to ecology. To evaluate a scientific agent's ability to collect informative experimental data, we compute the expected information gain (EIG), an information-theoretic quantity which measures how much an experiment reduces uncertainty about the parameters of a generative model. A good scientific theory is a concise and predictive explanation. To quantitatively evaluate model discovery, we ask a scientific agent to explain their model and evaluate whether this explanation helps another scientific agent make more accurate predictions. We evaluate several open and closed-source language models of varying sizes. We find that larger models (32B) consistently outperform smaller variants (7B), and that closed-source models generally achieve better results than open-source alternatives. However, all current approaches struggle with both experimental design and model discovery, highlighting these as promising directions for future research.

Primary Area: Datasets & Benchmarks for applications in language modeling and vision language modeling

Submission Number: 2194