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Large language models appear to learn facts from the large text corpora theyare trained on. Such facts are encoded implicitly within their many parameters,making it difficult to verify or manipulate what knowledge has been learned.Language models have recently been extended to multilingual language models(MLLMs), enabling knowledge to be learned across hundreds of languages.Meanwhile, knowledge graphs contain facts in an explicit triple format, whichrequire careful and costly curation and are only available in a fewhigh-resource languages, restricting their research and application. To addressthese issues, we propose to enhance MLLMs with knowledge from multilingualknowledge graphs (MLKGs) so as to tackle language and knowledge graph tasksacross many languages, including low-resource ones. Specifically, we introducea lightweight adapter set to enhance MLLMs with cross-lingual entity alignmentand facts from MLKGs for many languages. Experiments on common benchmarks showthat such enhancement benefits both MLLMs and MLKGs, achieving: (1) comparableor improved performance for knowledge graph completion and entity alignmentrelative to baselines, especially for low-resource languages (for whichknowledge graphs are unavailable); and (2) improved MLLM performance onlanguage understanding tasks that require multilingual factual knowledge; allwhile maintaining performance on other general language tasks.

Adapters for Enhanced Modeling of Multilingual Knowledge and Text