availability of resources natively in French is an
obvious limitation of our work. Regarding mod-
els, there are far fewer options than with more
widespread languages such as English. Indeed,
most of the existing French embedding models we
found are trained using either older architectures
or methods, unlike most recent multilingual mod-
els such as NV-Embed-v1 (Lee et al., 2024) or e5-
mistral-7b-instruct (Wang et al., 2023). Comparing
models by family would be beneficial, particularly
for evaluating French models against multilingual
models on the same architecture using the same
training technique. Resource limitations also ap-
ply to datasets. For example, the summarization
task dataset is translated, which can be less relevant
than a natively French dataset. We have also built
datasets for reranking tasks using existing ones
from retrieval task because we could not find any
in French. This construction process introduces a
bias as the model performance on both tasks may be
8