  Trustworthy answer content is abundant in many high-resource languages and is
instantly accessible through question answering systems, yet this content can
be hard to access for those that do not speak these languages. The leap forward
in cross-lingual modeling quality offered by generative language models offers
much promise, yet their raw generations often fall short in factuality. To
improve trustworthiness in these systems, a promising direction is to attribute
the answer to a retrieved source, possibly in a content-rich language different
from the query. Our work is the first to study attribution for cross-lingual
question answering. First, we collect data in 5 languages to assess the
attribution level of a state-of-the-art cross-lingual QA system. To our
surprise, we find that a substantial portion of the answers is not attributable
to any retrieved passages (up to 50% of answers exactly matching a gold
reference) despite the system being able to attend directly to the retrieved
text. Second, to address this poor attribution level, we experiment with a wide
range of attribution detection techniques. We find that Natural Language
Inference models and PaLM 2 fine-tuned on a very small amount of attribution
data can accurately detect attribution. Based on these models, we improve the
attribution level of a cross-lingual question-answering system. Overall, we
show that current academic generative cross-lingual QA systems have substantial
shortcomings in attribution and we build tooling to mitigate these issues.
