title: Evaluating Cognitive Maps and Planning in Large Language Models with
    CogEval
summary: Recently an influx of studies claim emergent cognitive abilities in large
  language models (LLMs). Yet, most rely on anecdotes, overlook contamination of
  training sets, or lack systematic Evaluation involving multiple tasks, control
  conditions, multiple iterations, and statistical robustness tests. Here we make
  two major contributions. First, we propose CogEval, a cognitive
  science-inspired protocol for the systematic evaluation of cognitive capacities
  in Large Language Models. The CogEval protocol can be followed for the
  evaluation of various abilities. Second, here we follow CogEval to
  systematically evaluate cognitive maps and planning ability across eight LLMs
  (OpenAI GPT-4, GPT-3.5-turbo-175B, davinci-003-175B, Google Bard,
  Cohere-xlarge-52.4B, Anthropic Claude-1-52B, LLaMA-13B, and Alpaca-7B). We base
  our task prompts on human experiments, which offer both established construct
  validity for evaluating planning, and are absent from LLM training sets. We
  find that, while LLMs show apparent competence in a few planning tasks with
  simpler structures, systematic evaluation reveals striking failure modes in
  planning tasks, including hallucinations of invalid trajectories and getting
  trapped in loops. These findings do not support the idea of emergent
  out-of-the-box planning ability in LLMs. This could be because LLMs do not
  understand the latent relational structures underlying planning problems, known
  as cognitive maps, and fail at unrolling goal-directed trajectories based on
  the underlying structure. Implications for application and future directions
  are discussed.
paper_authors._id: 6514e6896a38fb64d5acdfc7
paper_authors.name: Ida Momennejad
paper_authors.status: claimed_verified
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paper_authors.fullname: Ida
paper_authors.type: user
paper_authors.user: idamomen
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paper_authors.name: Hosein Hasanbeig
paper_authors._id: 6514e6896a38fb64d5acdfc9
paper_authors.name: Felipe Vieira
paper_authors._id: 6514e6896a38fb64d5acdfca
paper_authors.name: Hiteshi Sharma
paper_authors._id: 6514e6896a38fb64d5acdfcb
paper_authors.name: Robert Osazuwa Ness
paper_authors.status: admin_assigned
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paper_authors.fullname: Robert Osazuwa Ness
paper_authors.type: user
paper_authors.user: osazuwa
paper_authors._id: 6514e6896a38fb64d5acdfcc
paper_authors.name: Nebojsa Jojic
paper_authors._id: 6514e6896a38fb64d5acdfcd
paper_authors.name: Hamid Palangi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-28T09:13:42.634Z
paper_authors._id: 6189ddd0992df2640e3e7d40
paper_authors.avatarUrl: /avatars/925f2308fc412ae352b57a1b71815028.svg
paper_authors.fullname: Hamid Palangi
paper_authors.type: user
paper_authors.user: hamidpalangi
paper_authors._id: 6514e6896a38fb64d5acdfce
paper_authors.name: Jonathan Larson
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-28T19:03:45.591Z
paper_authors._id: 636176606bd72c97d00246cf
paper_authors.avatarUrl: /avatars/82f55e86db71af82480d63b4d04c1466.svg
paper_authors.fullname: Jonathan
paper_authors.type: user
paper_authors.user: JoLarso
paper_title: Evaluating Cognitive Maps and Planning in Large Language Models with
    CogEval
paper_summary: Recently an influx of studies claim emergent cognitive abilities in large
  language models (LLMs). Yet, most rely on anecdotes, overlook contamination of
  training sets, or lack systematic Evaluation involving multiple tasks, control
  conditions, multiple iterations, and statistical robustness tests. Here we make
  two major contributions. First, we propose CogEval, a cognitive
  science-inspired protocol for the systematic evaluation of cognitive capacities
  in Large Language Models. The CogEval protocol can be followed for the
  evaluation of various abilities. Second, here we follow CogEval to
  systematically evaluate cognitive maps and planning ability across eight LLMs
  (OpenAI GPT-4, GPT-3.5-turbo-175B, davinci-003-175B, Google Bard,
  Cohere-xlarge-52.4B, Anthropic Claude-1-52B, LLaMA-13B, and Alpaca-7B). We base
  our task prompts on human experiments, which offer both established construct
  validity for evaluating planning, and are absent from LLM training sets. We
  find that, while LLMs show apparent competence in a few planning tasks with
  simpler structures, systematic evaluation reveals striking failure modes in
  planning tasks, including hallucinations of invalid trajectories and getting
  trapped in loops. These findings do not support the idea of emergent
  out-of-the-box planning ability in LLMs. This could be because LLMs do not
  understand the latent relational structures underlying planning problems, known
  as cognitive maps, and fail at unrolling goal-directed trajectories based on
  the underlying structure. Implications for application and future directions
  are discussed.
paper_ai_keywords: cognitive science-inspired protocol CogEval cognitive capacities cognitive maps planning ability large language models (LLMs) OpenAI GPT-4 GPT-3.5-turbo-175B davinci-003-175B Google Bard Cohere-xlarge-52.4B Anthropic-1-52B LLaMA-13B Alpaca-7B human experiments construct validity hallucinations invalid trajectories loops goal-directed trajectories emergent out-of-the-box compartment