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Computer Science > Artificial Intelligence

arXiv:2501.18009 (cs)
[Submitted on 29 Jan 2025]

Title:Large Language Models Think Too Fast To Explore Effectively

Authors:Lan Pan, Hanbo Xie, Robert C. Wilson
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To Explore Effectively, by Lan Pan and 2 other authors
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    Abstract: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.

Comments: 16 pages, 13 figures, under review
Subjects: Artificial Intelligence (cs.AI); Neurons and Cognition
          (q-bio.NC)
Cite as:  arXiv:2501.18009 [cs.AI]
          (or arXiv:2501.18009v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2501.18009
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hanbo Xie [view email]
[v1] Wed, 29 Jan 2025 21:51:17 UTC (2,674 KB)
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