ALIGNed-LLM aligns knowledge graph entity embeddings with language model text embeddings through a trainable projection layer, improving question answering accuracy on KG-derived datasets.
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
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abstract
We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers. We run baselines over Mintaka, the best of which achieves 38% hits@1 in English and 31% hits@1 multilingually, showing that existing models have room for improvement. We release Mintaka at https://github.com/amazon-research/mintaka.
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Aligning Knowledge Graphs and Language Models for Factual Accuracy
ALIGNed-LLM aligns knowledge graph entity embeddings with language model text embeddings through a trainable projection layer, improving question answering accuracy on KG-derived datasets.