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text2code.md

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title Text2Code Generation

Goal

Our goal is to develop sytems for Text-to-Code generation, specifically Text-to-SQL, that are easy to customize to new schema, reliable, and efficient. A good overall pitch of our work (as of early 2024) can be found in these [slides]. For more recent work, please see our recent papers.

Publications

  • Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding. (Under review)

  • The Missing Alignment Link of In-context Learning on Sequences. Harshvardhan Agarwal, Sunita Sarawagi. In ICML 2025

  • Diverse In-Context Example Selection After Decomposing Programs and Aligned Utterances Improves Semantic Parsing. Mayank Kothyari, Sunita Sarawagi, Soumen Chakrabarti, Gaurav Arora, Srujana Merugu. In NAACL 2025

  • Benchmarking and Improving Text-to-SQL Generation under Ambiguity. Adithya Bhaskar, Tushar Tomar, Ashutosh Sathe, Sunita Sarawagi In EMNLP 2023.

  • CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL. Mayank Kothyari, Dhruva Dhingra, Sunita Sarawagi, and Soumen Chakrabarti In EMNLP 2023.

  • Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models. Harshit Varma, Abhijeet Awasthi and Sunita Sarawagi In ICML 2023.

  • In-Situ Text-Only Adaptation of Speech Models with Low-Overhead Speech Imputations Ashish Mittal, Sunita Sarawagi, and Preethi Jyothi In ICLR 2023.

  • Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers Abhijeet Awasthi, Soumen Chakrabarti, and Sunita Sarawagi In AAAI 2023.

  • Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers Abhijeet Awasthi, Ashutosh Sathe and Sunita Sarawagi. In EMNLP (Long paper) 2022.