Complementing GPT-3 with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata
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As the largest knowledge base, Wikidata is a massive source of knowledge, complementing large language models with well-structured data. In this paper, we present WikiWebQuestions, a high-quality knowledge base question answering benchmark for Wikidata. This new benchmark uses real-world human data with SPARQL annotation to facilitate a more accurate comparison with large language models utilizing the up-to-date answers from Wikidata. Additionally, a baseline for this benchmark is established with an effective training data synthesis methodology and WikiSP, a Seq2Seq semantic parser, that handles large noisy knowledge graphs. Experimental results illustrate the effectiveness of this methodology, achieving 69 respectively. We showed that we can pair semantic parsers with GPT-3 to provide a combination of verifiable results and qualified guesses that can provide useful answers to 97
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