title: Large Language Models Think Too Fast To Explore Effectively
summary: 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.
paper_authors._id: 679c5b0259e9218a222ab742
paper_authors.name: Lan Pan
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-31T06:33:49.785Z
paper_authors._id: 6689f7fb8c440fe1955a51b5
paper_authors.avatarUrl: /avatars/9b23ee2f05f55615c6174a678436b30d.svg
paper_authors.fullname: Lan Pan
paper_authors.type: user
paper_authors.user: louanna
paper_authors._id: 679c5b0259e9218a222ab743
paper_authors.name: Hanbo Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-31T08:51:40.573Z
paper_authors._id: 63fd543a3c880680af459cad
paper_authors.avatarUrl: /avatars/d7ca320380cc98918c8aaa33790babec.svg
paper_authors.fullname: Hanbo Xie
paper_authors.type: user
paper_authors.user: xhb120633
paper_authors._id: 679c5b0259e9218a222ab744
paper_authors.name: Robert C. Wilson
paper_title: Large Language Models Think Too Fast To Explore Effectively
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) Little Alchemy 2 Sparse Autoencoders transformer blocks