{"type":"ScholarlyArticle","title":"LLM-powered GraphQL Generator for Data Retrieval","authors":[{"name":"Balaji Ganesan","slug":"balaji-ganesan","given":"Balaji","family":"Ganesan","affiliation":"IBM Research"},{"name":"Sambit Ghosh","slug":"sambit-ghosh","given":"Sambit","family":"Ghosh","affiliation":"IBM Research"},{"name":"Nitin Gupta","slug":"nitin-gupta","given":"Nitin","family":"Gupta","affiliation":"IBM Research"},{"name":"Manish Kesarwani","slug":"manish-kesarwani","given":"Manish","family":"Kesarwani","affiliation":"IBM Research"},{"name":"Sameep Mehta","slug":"sameep-mehta","given":"Sameep","family":"Mehta","affiliation":"IBM Research"},{"name":"Renuka Sindhgatta","slug":"renuka-sindhgatta","given":"Renuka","family":"Sindhgatta","affiliation":"IBM Research"}],"abstract":"GraphQL offers an efficient, powerful, and flexible alternative to REST APIs. However, application developers writing GraphQL clients need both technical and domain-specific expertise to reap its benefits, and avoid over-fetching or under-fetching data. Automated GraphQL generation has so far proven to be a hard problem because of complex GraphQL schema and lack of benchmark datasets. To address these issues, our work focuses on building an LLM-powered pipeline that can accept user requirements in natural language along with the complex GraphQL schema and automatically produce the GraphQL query needed to retrieve the necessary data. Automated GraphQL generation helps reduce entry barriers to application developers, broadening GraphQL adoption.","doi":"10.24963/ijcai.2024/1002","url":"https://ijcai.org/proceedings/2024/1002","pdf":"https://ijcai.org/proceedings/2024/1002.pdf","markdown":"https://ijcai.org/proceedings/2024/1002.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1002","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1002,"pages":{"first":8657,"last":8660},"keywords":["Data Mining: DM: Applications","Natural Language Processing: NLP: Applications"],"videos":[],"references":[{"position":1,"raw":"[Apollo Graph Inc, 2024] Apollo Graph Inc. 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