  In this paper, I describe methodological considerations for studies that aim
to evaluate the cognitive capacities of large language models (LLMs) using
language-based behavioral assessments. Drawing on three case studies from the
literature (a commonsense knowledge benchmark, a theory of mind evaluation, and
a test of syntactic agreement), I describe common pitfalls that might arise
when applying a cognitive test to an LLM. I then list 10 do's and don'ts that
should help design high-quality cognitive evaluations for AI systems. I
conclude by discussing four areas where the do's and don'ts are currently under
active discussion -- prompt sensitivity, cultural and linguistic diversity,
using LLMs as research assistants, and running evaluations on open vs. closed
LLMs. Overall, the goal of the paper is to contribute to the broader discussion
of best practices in the rapidly growing field of AI Psychology.
