Evaluating Cognitive Maps and Planning in Large Language Models with
  CogEval
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.
6514e6896a38fb64d5acdfc7
Ida Momennejad
claimed_verified
2023-09-29T07:04:31.787Z
6515f2f399e8060f1e80642d
https://cdn-avatars.huggingface.co/v1/production/uploads/6515f2f399e8060f1e80642d/-XaZu6zvYeTRYPgRtLSTW.jpeg
Ida
user
idamomen
6514e6896a38fb64d5acdfc8
Hosein Hasanbeig
6514e6896a38fb64d5acdfc9
Felipe Vieira
6514e6896a38fb64d5acdfca
Hiteshi Sharma
6514e6896a38fb64d5acdfcb
Robert Osazuwa Ness
admin_assigned
2023-09-28T09:13:27.948Z
6384e1e9b2906edaf835dea4
/avatars/1087a75f7dbfd65b4d18d369d38eb5be.svg
Robert Osazuwa Ness
user
osazuwa
6514e6896a38fb64d5acdfcc
Nebojsa Jojic
6514e6896a38fb64d5acdfcd
Hamid Palangi
admin_assigned
2023-09-28T09:13:42.634Z
6189ddd0992df2640e3e7d40
/avatars/925f2308fc412ae352b57a1b71815028.svg
Hamid Palangi
user
hamidpalangi
6514e6896a38fb64d5acdfce
Jonathan Larson
claimed_verified
2023-09-28T19:03:45.591Z
636176606bd72c97d00246cf
/avatars/82f55e86db71af82480d63b4d04c1466.svg
Jonathan
user
JoLarso
Evaluating Cognitive Maps and Planning in Large Language Models with
  CogEval
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.
Systematic evaluation of cognitive maps and planning abilities in multiple large language models reveals significant failures in planning tasks and lack of understanding of underlying cognitive structures.
CogEval Large Language Models LLMs cognitive maps planning ability task prompts human experiments construct validity training sets hallucinations goal-directed trajectories