title: Cognitive Map for Language Models: Optimal Planning via Verbally
    Representing the World Model
summary: Language models have demonstrated impressive capabilities across various
  natural language processing tasks, yet they struggle with planning tasks
  requiring multi-step simulations. Inspired by human cognitive processes, this
  paper investigates the optimal planning power of language models that can
  construct a cognitive map of a given environment. Our experiments demonstrate
  that cognitive map significantly enhances the performance of both optimal and
  reachable planning generation ability in the Gridworld path planning task. We
  observe that our method showcases two key characteristics similar to human
  cognition: generalization of its planning ability to extrapolated
  environments and rapid adaptation with limited training data. We hope our
  findings in the Gridworld task provide insights into modeling human cognitive
  processes in language models, potentially leading to the development of more
  advanced and robust systems that better resemble human cognition.
paper_authors._id: 667913fcbe02576885d0d997
paper_authors.name: Doyoung Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T13:41:25.886Z
paper_authors._id: 610659fc377b3983fe49b3a7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1665487693252-610659fc377b3983fe49b3a7.jpeg
paper_authors.fullname: doyoung kim
paper_authors.type: user
paper_authors.user: doyoungkim
paper_authors._id: 667913fcbe02576885d0d998
paper_authors.name: Jongwon Lee
paper_authors._id: 667913fcbe02576885d0d999
paper_authors.name: Jinho Park
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T13:42:00.666Z
paper_authors._id: 638467ee8283412d401770dd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/638467ee8283412d401770dd/UAMwDHhSwf91XSsubfrS_.jpeg
paper_authors.fullname: Jinho Park
paper_authors.type: user
paper_authors.user: Br3ad
paper_authors._id: 667913fcbe02576885d0d99a
paper_authors.name: Minjoon Seo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T13:42:07.059Z
paper_authors._id: 621f05ba970615ad5861ceb1
paper_authors.avatarUrl: /avatars/7e1902aa71369a524afda9b0a9e88e22.svg
paper_authors.fullname: Minjoon Seo
paper_authors.type: user
paper_authors.user: minjoon
paper_title: Cognitive Map for Language Models: Optimal Planning via Verbally
    Representing the World Model
paper_summary: Language models have demonstrated impressive capabilities across various
  natural language processing tasks, yet they struggle with planning tasks
  requiring multi-step simulations. Inspired by human cognitive processes, this
  paper investigates the optimal planning power of language models that can
  construct a cognitive map of a given environment. Our experiments demonstrate
  that cognitive map significantly enhances the performance of both optimal and
  reachable planning generation ability in the Gridworld path planning task. We
  observe that our method showcases two key characteristics similar to human
  cognition: generalization of its planning ability to extrapolated
  environments and rapid adaptation with limited training data. We hope our
  findings in the Gridworld task provide insights into modeling human cognitive
  processes in language models, potentially leading to the development of more
  advanced and robust systems that better resemble human cognition.
paper_ai_summary: Cognitive maps in language models improve their planning ability and adaptability in gridworld environments, mimicking human cognitive processes.
paper_ai_keywords: cognitive map optimal planning reachable planning gridworld planning generation generalization adaptation cognitive processes language models