Papers by Sanjay Krishna Gouda
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. |
Token Alignment via Character Matching for Subword Completion (2024.findings-acl)
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Ben Athiwaratkun, Shiqi Wang, Mingyue Shang, Yuchen Tian, Zijian Wang, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Robert Kwiatkowski, Ramesh Nallapati, Parminder Bhatia, Bing Xiang
| Challenge: | Generative models struggle with prompts corresponding to partial tokens due to tokenization, where partial token is out-of-distribution during inference. |
| Approach: | They propose a method to alleviate tokenization artifact on text completion by backtracking to the last complete tokens and aligning subsequent generations to match with the prompt. |
| Outcome: | The proposed method shows that it improves on partial token scenarios with only a minor time increase. |
CodeFort: Robust Training for Code Generation Models (2024.findings-emnlp)
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Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
| Challenge: | Existing research efforts to improve code generation models are inadequate . code generation model performance is degraded under small perturbations . |
| Approach: | They propose a framework to improve the robustness of code generation models by generalizing code perturbations to enrich training data and enabling various robust training strategies. |
| Outcome: | The proposed framework increases pass rates and robustness drop rate against code-syntax perturbations. |