Large Language Models Think Too Fast To Explore Effectively
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.
679c5b0259e9218a222ab742
Lan Pan
extracted_confirmed
2025-01-31T06:33:49.785Z
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Lan Pan
user
louanna
679c5b0259e9218a222ab743
Hanbo Xie
admin_assigned
2025-01-31T08:51:40.573Z
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/avatars/d7ca320380cc98918c8aaa33790babec.svg
Hanbo Xie
user
xhb120633
679c5b0259e9218a222ab744
Robert C. Wilson
Large Language Models Think Too Fast To Explore Effectively
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.
LLMs generally underperform humans in exploration tasks, relying more on uncertainty-driven strategies and making premature decisions, unlike humans who balance uncertainty and empowerment.
Large Language Models Little Alchemy 2 exploration uncertainty-driven strategies empowerment Sparse Autoencoders transformer blocks