Papers by Avirup Saha
Mind the Query: A Benchmark Dataset towards Text2Cypher Task (2025.emnlp-industry)
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| Challenge: | Graph databases store data in nodes and relationships, enabling more natural modeling of complex, interconnected data. |
| Approach: | They present a high-quality dataset for the Text2Cypher task . it is enabling the translation of natural language (NL) questions into executable Cypher queries over graph databases. |
| Outcome: | The proposed dataset includes 27,529 NL queries and corresponding Cyphers spanning across 11 real-world graph datasets. |
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. |
tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets (2021.naacl-main)
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| Challenge: | Existing stance detection datasets are complex deep neural networks, making them difficult to interpret. |
| Approach: | They propose a new large dataset free of such biases and demonstrate its aptness on existing stance detection systems. |
| Outcome: | The proposed model achieves human-level performance on the WT–WT dataset and more than two-third accuracy on other datasets. |
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification (N19-1)
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| Challenge: | Existing fact-checking datasets do not provide manual annotations for sentence-level evidence. |
| Approach: | They propose a task-agnostic pipelined system that extracts textual evidence that supports or refutes a factual claim from Wikipedia pages. |
| Outcome: | The proposed system achieves state-of-the-art results on the FEVER dataset. |