Papers by Carlos Eberhardt
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. |
Sequential API Function Calling Using GraphQL Schema (2024.emnlp-main)
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Avirup Saha, Lakshmi Mandal, Balaji Ganesan, Sambit Ghosh, Renuka Sindhgatta, Carlos Eberhardt, Dan Debrunner, Sameep Mehta
| Challenge: | Function calling using Large Language Models (LLMs) is an active research area . however, sequential function calling using LLMs with interdependence between functions is still under-explored . |
| Approach: | They propose a dataset representing real-world REST API calls with variable mapping between functions using natural language utterances paired with function call sequences. |
| Outcome: | The proposed dataset represents real-world REST API calls with variable mapping between functions. |
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. |