Papers by Rajarishi Sinha
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data (2023.findings-emnlp)
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| Challenge: | Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text. |
| Approach: | They propose a method to improve few-shot prompting capabilities of Text-to-SQL for Large Language Models (LLMs) they propose 'SQlPrompt' which aims to diversify the SQL proposals during consistency selection with different prompt designs and foundation models. |
| Outcome: | The proposed method outperforms previous approaches for in-context learning with zero labeled data by a large margin, closing the gap with finetuning state-of-the-art with thousands of labeles. |
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions (2024.emnlp-main)
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| Challenge: | Embeddings from Large Language Models (LLMs) have emerged as critical components in information retrieval applications. |
| Approach: | They propose a tuning framework for the customization of LLM embeddings. |
| Outcome: | The proposed framework reduces embedding dimensions while maintaining comparable performance levels. |