Papers by Mayank Kothyari
Diverse In-Context Example Selection After Decomposing Programs and Aligned Utterances Improves Semantic Parsing (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are well suited for seq2seq translation . a lack of pretraining corpora can hinder the use of LLMs for structured interpretation . |
| Approach: | They propose to decompose available ICE trees into fragments and use additional invocations to map them to corresponding utterances. |
| Outcome: | The proposed method shows visible gains on diverse parsing benchmarks on popular languages. |
CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL (2023.emnlp-main)
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| Challenge: | Existing Text-to-SQL generators require the entire schema to be encoded with the user text. |
| Approach: | They propose a method that uses an LLM to hallucinate a minimal DB schema . they use the hallucinated schema to retrieve a subset of the actual schema based on multiple dense retrievals . |
| Outcome: | The proposed method leads to significantly higher recall than existing methods. |
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation (2023.acl-long)
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Suraj Kothawade, Anmol Mekala, D.Chandra Sekhara Hetha Havya, Mayank Kothyari, Rishabh Iyer, Ganesh Ramakrishnan, Preethi Jyothi
| Challenge: | State-of-the-art automatic speech recognition systems exhibit disparate performance on varying speech accents. |
| Approach: | They propose to use submodular mutual information to find the most informative set of utterances matching a target accent within a fixed budget. |
| Outcome: | The proposed model is 3-5 times more label-efficient on the Indic-TTS and L2 datasets than other methods. |