@inproceedings{
piriyakulkij2024doing,
title={Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning},
author={Wasu Top Piriyakulkij and Cassidy Langenfeld and Tuan Anh Le and Kevin Ellis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HXdAfK488A}
}
We give a model of how to infer natural language rules by doing experiments. The
model integrates Large Language Models (LLMs) with Monte Carlo algorithms for
probabilistic inference, interleaving online belief updates with experiment design
under information-theoretic criteria. We conduct a human-model comparison on a
Zendo-style task, finding that a critical ingredient for modeling the human data is to
assume that humans also consider fuzzy, probabilistic rules, in addition to assuming
that humans perform approximately-Bayesian belief updates. We also compare
with recent algorithms for using LLMs to generate and revise hypotheses, finding
that our online inference method yields higher accuracy at recovering the true
underlying rule, and provides better support for designing optimal experiments.
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning
Wasu Top Piriyakulkij Cassidy Langenfeld Tuan Anh Le Kevin Ellis