@inproceedings{gupta-etal-2025-schema,
title = "Schema and Natural Language Aware In-Context Learning for Improved {G}raph{QL} Query Generation",
author = "Gupta, Nitin and
Kesarwani, Manish and
Ghosh, Sambit and
Mehta, Sameep and
Eberhardt, Carlos and
Debrunner, Dan",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.76/",
doi = "10.18653/v1/2025.naacl-industry.76",
pages = "1009--1015",
ISBN = "979-8-89176-194-0",
abstract = "GraphQL offers a flexible alternative to REST APIs, allowing precise data retrieval across multiple sources in a single query. However, generating complex GraphQL queries remains a significant challenge. Large Language Models (LLMs), while powerful, often produce suboptimal queries due to limited exposure to GraphQL schemas and their structural intricacies.Custom prompt engineering with in-context examples is a common approach to guide LLMs, but existing methods, like randomly selecting examples, often yield unsatisfactory results. While semantic similarity-based selection is effective in other domains, it falls short for GraphQL, where understanding schema-specific nuances is crucial for accurate query formulation.To address this, we propose a Schema and NL-Aware In-context Learning (SNAIL) framework that integrates both structural and semantic information from GraphQL schemas with natural language inputs, enabling schema-aware in-context learning. Unlike existing methods, our approach captures the complexities of GraphQL schemas to improve query generation accuracy.We validate this framework on a publicly available complex GraphQL test dataset, demonstrating notable performance improvements, with specific query classes showing up to a 20{\%} performance improvement for certain LLMs. As GraphQL adoption grows, with Gartner predicting over 60{\%} of enterprises will use it in production by 2027, this work addresses a critical need, paving the way for more efficient and reliable GraphQL query generation in enterprise applications."
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<abstract>GraphQL offers a flexible alternative to REST APIs, allowing precise data retrieval across multiple sources in a single query. However, generating complex GraphQL queries remains a significant challenge. Large Language Models (LLMs), while powerful, often produce suboptimal queries due to limited exposure to GraphQL schemas and their structural intricacies.Custom prompt engineering with in-context examples is a common approach to guide LLMs, but existing methods, like randomly selecting examples, often yield unsatisfactory results. While semantic similarity-based selection is effective in other domains, it falls short for GraphQL, where understanding schema-specific nuances is crucial for accurate query formulation.To address this, we propose a Schema and NL-Aware In-context Learning (SNAIL) framework that integrates both structural and semantic information from GraphQL schemas with natural language inputs, enabling schema-aware in-context learning. Unlike existing methods, our approach captures the complexities of GraphQL schemas to improve query generation accuracy.We validate this framework on a publicly available complex GraphQL test dataset, demonstrating notable performance improvements, with specific query classes showing up to a 20% performance improvement for certain LLMs. As GraphQL adoption grows, with Gartner predicting over 60% of enterprises will use it in production by 2027, this work addresses a critical need, paving the way for more efficient and reliable GraphQL query generation in enterprise applications.</abstract>
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%0 Conference Proceedings
%T Schema and Natural Language Aware In-Context Learning for Improved GraphQL Query Generation
%A Gupta, Nitin
%A Kesarwani, Manish
%A Ghosh, Sambit
%A Mehta, Sameep
%A Eberhardt, Carlos
%A Debrunner, Dan
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F gupta-etal-2025-schema
%X GraphQL offers a flexible alternative to REST APIs, allowing precise data retrieval across multiple sources in a single query. However, generating complex GraphQL queries remains a significant challenge. Large Language Models (LLMs), while powerful, often produce suboptimal queries due to limited exposure to GraphQL schemas and their structural intricacies.Custom prompt engineering with in-context examples is a common approach to guide LLMs, but existing methods, like randomly selecting examples, often yield unsatisfactory results. While semantic similarity-based selection is effective in other domains, it falls short for GraphQL, where understanding schema-specific nuances is crucial for accurate query formulation.To address this, we propose a Schema and NL-Aware In-context Learning (SNAIL) framework that integrates both structural and semantic information from GraphQL schemas with natural language inputs, enabling schema-aware in-context learning. Unlike existing methods, our approach captures the complexities of GraphQL schemas to improve query generation accuracy.We validate this framework on a publicly available complex GraphQL test dataset, demonstrating notable performance improvements, with specific query classes showing up to a 20% performance improvement for certain LLMs. As GraphQL adoption grows, with Gartner predicting over 60% of enterprises will use it in production by 2027, this work addresses a critical need, paving the way for more efficient and reliable GraphQL query generation in enterprise applications.
%R 10.18653/v1/2025.naacl-industry.76
%U https://aclanthology.org/2025.naacl-industry.76/
%U https://doi.org/10.18653/v1/2025.naacl-industry.76
%P 1009-1015
Markdown (Informal)
[Schema and Natural Language Aware In-Context Learning for Improved GraphQL Query Generation](https://aclanthology.org/2025.naacl-industry.76/) (Gupta et al., NAACL 2025)
ACL
- Nitin Gupta, Manish Kesarwani, Sambit Ghosh, Sameep Mehta, Carlos Eberhardt, and Dan Debrunner. 2025. Schema and Natural Language Aware In-Context Learning for Improved GraphQL Query Generation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 1009–1015, Albuquerque, New Mexico. Association for Computational Linguistics.