Papers by Renuka Sindhgatta
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. |
Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System (N19-2)
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Shazia Afzal, Tejas Dhamecha, Nirmal Mukhi, Renuka Sindhgatta, Smit Marvaniya, Matthew Ventura, Jessica Yarbro
| Challenge: | Dialog-based intelligent tutoring systems capture the effectiveness of expert human teacher-learner interactions by using natural language dialogue. |
| Approach: | They propose to use dialog-based tutoring systems to help students learn through a sequence of dialogue moves in natural language to steer them through varying levels of content granularity. |
| Outcome: | The proposed system is being used by hundreds of college level students for practice and self-regulated study in diverse subjects like Sociology, Communications, and American Government. |
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. |