  Large Language Models (LLMs) like ChatGPT or Bard have revolutionized
information retrieval and captivated the audience with their ability to
generate custom responses in record time, regardless of the topic. In this
article, we assess the capabilities of various LLMs in producing reliable,
comprehensive, and sufficiently relevant responses about historical facts in
French. To achieve this, we constructed a testbed comprising numerous
history-related questions of varying types, themes, and levels of difficulty.
Our evaluation of responses from ten selected LLMs reveals numerous
shortcomings in both substance and form. Beyond an overall insufficient
accuracy rate, we highlight uneven treatment of the French language, as well as
issues related to verbosity and inconsistency in the responses provided by
LLMs.
