Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in
  Multilingual Language Models
Sara Rajaee Christof Monz
Recent advances in training multilingual language models on large datasets
seem to have shown promising results in knowledge transfer across languages and
achieve high performance on downstream tasks. However, we question to what
extent the current evaluation benchmarks and setups accurately measure
zero-shot cross-lingual knowledge transfer. In this work, we challenge the
assumption that high zero-shot performance on target tasks reflects high
cross-lingual ability by introducing more challenging setups involving
instances with multiple languages. Through extensive experiments and analysis,
we show that the observed high performance of multilingual models can be
largely attributed to factors not requiring the transfer of actual linguistic
knowledge, such as task- and surface-level knowledge. More specifically, we
observe what has been transferred across languages is mostly data artifacts and
biases, especially for low-resource languages. Our findings highlight the
overlooked drawbacks of existing cross-lingual test data and evaluation setups,
calling for a more nuanced understanding of the cross-lingual capabilities of
multilingual models.