1 code implementation • 31 May 2023 • Nghi D. Q. Bui, Hung Le, Yue Wang, Junnan Li, Akhilesh Deepak Gotmare, Steven C. H. Hoi
In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence.
2 code implementations • 13 May 2023 • Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D. Q. Bui, Junnan Li, Steven C. H. Hoi
To address these limitations, we propose ``CodeT5+'', a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks.
Ranked #1 on Code Search on CodeXGLUE - AdvTest
2 code implementations • 5 Jul 2022 • Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C. H. Hoi
To address the limitations, we propose "CodeRL", a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning (RL).
Ranked #1 on Code Generation on APPS
no code implementations • 15 Oct 2021 • Akhilesh Deepak Gotmare, Junnan Li, Shafiq Joty, Steven C. H. Hoi
The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query.
5 code implementations • NeurIPS 2021 • Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, Steven Hoi
Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens.
Ranked #5 on Open Vocabulary Attribute Detection on OVAD-Box benchmark (using extra training data)
3 code implementations • Findings (EMNLP) 2021 • Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani
While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate.