@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://preview.aclanthology.org/fix-sig-urls/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."
}