Cognitive Map for Language Models: Optimal Planning via Verbally
  Representing the World Model
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.
667913fcbe02576885d0d997
Doyoung Kim
admin_assigned
2024-06-24T13:41:25.886Z
610659fc377b3983fe49b3a7
https://cdn-avatars.huggingface.co/v1/production/uploads/1665487693252-610659fc377b3983fe49b3a7.jpeg
doyoung kim
user
doyoungkim
667913fcbe02576885d0d998
Jongwon Lee
667913fcbe02576885d0d999
Jinho Park
admin_assigned
2024-06-24T13:42:00.666Z
638467ee8283412d401770dd
https://cdn-avatars.huggingface.co/v1/production/uploads/638467ee8283412d401770dd/UAMwDHhSwf91XSsubfrS_.jpeg
Jinho Park
user
jinhopark-ai
667913fcbe02576885d0d99a
Minjoon Seo
admin_assigned
2024-06-24T13:42:07.059Z
621f05ba970615ad5861ceb1
/avatars/7e1902aa71369a524afda9b0a9e88e22.svg
Minjoon Seo
user
minjoon
Cognitive Map for Language Models: Optimal Planning via Verbally
  Representing the World Model
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.
Cognitive maps in language models improve their planning ability and adaptability in gridworld environments, mimicking human cognitive processes.
cognitive map optimal planning reachable planning gridworld planning generation generalization adaptation cognitive processes language models