Large Language Models Think Too Fast To Explore Effectively
Authors: name: Lan Pan, name: Hanbo Xie, name: Robert C. Wilson

Large Language Models have emerged many intellectual capacities. While
numerous benchmarks assess their intelligence, limited attention has been given
to their ability to explore, an essential capacity for discovering new
information and adapting to novel environments in both natural and artificial
systems. The extent to which LLMs can effectively explore, particularly in
open-ended tasks, remains unclear. This study investigates whether LLMs can
surpass humans in exploration during an open-ended task, using Little Alchemy 2
as a paradigm, where agents combine elements to discover new ones. Results show
most LLMs underperform compared to humans, except for the o1 model, with those
traditional LLMs relying primarily on uncertainty driven strategies, unlike
humans who balance uncertainty and empowerment. Representational analysis of
the models with Sparse Autoencoders revealed that uncertainty and choices are
represented at earlier transformer blocks, while empowerment values are
processed later, causing LLMs to think too fast and make premature decisions,
hindering effective exploration. These findings shed light on the limitations
of LLM exploration and suggest directions for improving their adaptability.