Papers by Sameep Mehta
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. |
Divide, Link, and Conquer: Recall-oriented Schema Linking for NL-to-SQL via Question Decomposition (2025.emnlp-industry)
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| Challenge: | Open-source LLMs often depend on large proprietary models, which introduce serious privacy concerns. |
| Approach: | They propose a plug-and-play framework that improves SQL generation for smaller LLMs . they propose to apply question decomposition at the schema linking stage rather than during SQL generation . |
| Outcome: | The proposed framework improves schema linking recall by 25.1% and execution accuracy by 8.2% on the BIRD benchmark. |
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. |
CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation (2023.findings-acl)
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| Challenge: | Existing methods to control attributes of Language Models (LMs) for text generation are not safe, as toxicity and bias goals are opposed to each other. |
| Approach: | They propose a method to control the attributes of Language Models (LMs) for the text generation task using Causal Average Treatment Effect (ATE) scores and counterfactual augmentation. |
| Outcome: | The proposed architecture achieves state of the art performance for toxic degeneration, which are computed using Real Toxicity Prompts. |
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. |
Quality Assessment of Tabular Data using Large Language Models and Code Generation (2025.emnlp-industry)
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| Challenge: | Data quality is vital for business decisions; poor data quality costs organizations an average of $12.9 million annually. |
| Approach: | They propose a framework that combines statistical inliner detection with LLM-driven rule and code generation. |
| Outcome: | The proposed framework produces semantically valid quality rules and validates them with retrieval-augmented generation (RAG) Extensive evaluations on benchmark datasets confirm the effectiveness of the proposed framework. |
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%. |
CodeGenWrangler: Data Wrangling task automation using Code-Generating Models (2025.naacl-industry)
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| Challenge: | Tabular datasets in industrial settings often encompass extensive data with numerous rows and columns. |
| Approach: | They propose a system that leverages large language models to generate executable code for data wrangling tasks . they identify inherent patterns in the data while leveraging external knowledge . |
| Outcome: | The proposed system detects patterns in the data while leveraging external knowledge . it generates executable code for data-wrangling tasks like missing value imputation and error correction . |