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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Fedor Moiseev
- sl:arxiv_num : 2205.08184
- sl:arxiv_published : 2022-05-17T09:12:22Z
- sl:arxiv_summary : Large language models (LLMs) have demonstrated human-level performance on a
vast spectrum of natural language tasks. However, it is largely unexplored
whether they can better internalize knowledge from a structured data, such as a
knowledge graph, or from text. In this work, we propose a method to infuse
structured knowledge into LLMs, by directly training T5 models on factual
triples of knowledge graphs (KGs). We show that models pre-trained on Wikidata
KG with our method outperform the T5 baselines on FreebaseQA and WikiHop, as
well as the Wikidata-answerable subset of TriviaQA and NaturalQuestions. The
models pre-trained on factual triples compare competitively with the ones on
natural language sentences that contain the same knowledge. Trained on a
smaller size KG, WikiMovies, we saw 3x improvement of exact match score on
MetaQA task compared to T5 baseline. The proposed method has an advantage that
no alignment between the knowledge graph and text corpus is required in
curating training data. This makes our method particularly useful when working
with industry-scale knowledge graphs.@en
- sl:arxiv_title : SKILL: Structured Knowledge Infusion for Large Language Models@en
- sl:arxiv_updated : 2022-05-17T09:12:22Z
- sl:bookmarkOf : https://arxiv.org/abs/2205.08184
- sl:creationDate : 2022-05-18
- sl:creationTime : 2022-05-18T23:57:17Z
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