no code implementations • 16 Apr 2024 • Hantian Ding, Zijian Wang, Giovanni Paolini, Varun Kumar, Anoop Deoras, Dan Roth, Stefano Soatto
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens.
no code implementations • 13 Mar 2024 • Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Haifeng Qian, Hantian Ding, Qing Sun, Jun Wang, Jiacheng Guo, Liangfu Chen, Parminder Bhatia, Ramesh Nallapati, Sudipta Sengupta, Bing Xiang
In our study, we present bifurcated attention, a method developed for language model inference in single-context batch sampling contexts.
no code implementations • 2 Feb 2024 • Dejiao Zhang, Wasi Ahmad, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang
Recent studies have shown that code language models at scale demonstrate significant performance gains on downstream tasks, i. e., code generation.
no code implementations • 5 Jul 2023 • Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Xiaofei Ma, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance.
no code implementations • NAACL (ACL) 2022 • Hantian Ding, Jinrui Yang, Yuqian Deng, Hongming Zhang, Dan Roth
We introduce an open-domain topic classification system that accepts user-defined taxonomy in real time.
no code implementations • 5 Jun 2023 • Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang
Large language models trained on code have shown great potential to increase productivity of software developers.
2 code implementations • 26 Oct 2022 • Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
1 code implementation • ACL 2019 • Hongming Zhang, Hantian Ding, Yangqiu Song
Selectional Preference (SP) is a commonly observed language phenomenon and proved to be useful in many natural language processing tasks.