Evaluating and Modeling Attribution for Cross-Lingual Question Answering

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 introduce the XOR-AttriQA dataset to assess the attribution level of a state-of-the-art cross-lingual question answering (QA) system in 5 languages. To our surprise, we find that a substantial portion of the answers is not attributable to any retrieved passages (up to 47% 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. With these models, we improve the attribution level of a cross-lingual QA 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.