Evaluating Cognitive Maps and Planning in Large Language Models with
  CogEval
Ida Momennejad Hosein Hasanbeig Felipe Vieira Hiteshi Sharma Robert Osazuwa Ness Nebojsa Jojic Hamid Palangi Jonathan Larson
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.