Papers by Nitin Gupta
Schema and Natural Language Aware In-Context Learning for Improved GraphQL Query Generation (2025.naacl-industry)
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| Challenge: | GraphQL is a flexible alternative to REST APIs, but generating complex queries remains challenging. |
| Approach: | They propose a framework that integrates GraphQL schemas with natural language inputs to improve query generation accuracy. |
| Outcome: | The proposed framework improves performance on a publicly available complex GraphQL dataset. |
GraphQL Query Generation: A Large Training and Benchmarking Dataset (2024.emnlp-industry)
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Manish Kesarwani, Sambit Ghosh, Nitin Gupta, Shramona Chakraborty, Renuka Sindhgatta, Sameep Mehta, Carlos Eberhardt, Dan Debrunner
| Challenge: | GraphQL is a powerful query language for APIs, but crafting complex GraphqL queries can be challenging. |
| Approach: | a team of researchers has created a large-scale, cross-domain text-to-GraphQL query operation dataset . the dataset includes 10,940 training triples spanning 185 cross-source data stores and 957 test triples over 14 data stores. |
| Outcome: | The proposed dataset includes 10,940 training triples and 957 test triples over 14 data stores. |
Group, Embed and Reason: A Hybrid LLM and Embedding Framework for Semantic Attribute Alignment (2025.emnlp-industry)
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Shramona Chakraborty, Shashank Mujumdar, Nitin Gupta, Sameep Mehta, Ronen Kat, Itay Etelis, Mohamed Mahameed, Itai Guez, Rachel Tzoref-Brill
| Challenge: | a framework to align attributes that refer to the same concept but differ across schemas is challenging in schema only settings where no instance data is available due to ambiguous names, inconsistent descriptions, and domain-specific terminologies. |
| Approach: | They propose a framework that combines contextual reasoning and embedding-based similarity to address token limitations and hallucinations. |
| Outcome: | The proposed framework scales to large schemas and shows strong performance on healthcare schemas. |
Classifier-Augmented Generation for Structured Workflow Prediction (2025.emnlp-industry)
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| Challenge: | a new system translates natural language descriptions into executable workflows . configuring stages and their properties is time consuming and requires deep tool knowledge. |
| Approach: | They propose a system that translates natural language descriptions into executable workflows . it uses a Classifier-Augmented Generation approach that combines utterance decomposition with a classifier and stage-specific prompting to produce accurate stage predictions. |
| Outcome: | The proposed system outperforms existing models and reduces token usage by 60%. |