Papers by Sudipta Sengupta
A Static Evaluation of Code Completion by Large Language Models (2023.acl-industry)
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Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta
| Challenge: | Large language models trained on code have shown great potential to increase productivity of software developers. |
| Approach: | They propose a static evaluation framework to quantify static errors in Python code completions by leveraging Abstract Syntax Trees. |
| Outcome: | The proposed framework is more efficient and applicable to code in the wild. |
BASS: Batched Attention-optimized Speculative Sampling (2024.findings-acl)
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Haifeng Qian, Sujan Kumar Gonugondla, Sungsoo Ha, Mingyue Shang, Sanjay Krishna Gouda, Ramesh Nallapati, Sudipta Sengupta, Xiaofei Ma, Anoop Deoras
| Challenge: | Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. |
| Approach: | They propose a batched speculative decoding system that generates sequences at an average speed of 5.8ms per token and a batch size of 8 at a 2.15 speed-up over optimized regular decoding. |
| Outcome: | The proposed system achieves state-of-the-art latency and speed-up over optimized regular decoding. |
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding (2022.emnlp-industry)
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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta
| Challenge: | Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query. |
| Approach: | They propose a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. |
| Outcome: | The proposed framework outperforms the state-of-the-art model by 2.7% on a WikiTableQuestions test set. |