Nihal Nayak

nayak_brown22.jpeg

I am a Ph.D. candidate in Computer Science at Brown University. I work with Stephen Bach on zero-shot learning in language and vision.

CV (Updated in December, 2023)

Email : nnayak2 [at] cs [dot] brown [dot] edu

news

Feb 28, 2024 Excited to share new preprint on adapting large language models to tasks in specialized domains using Bonito, an open-source model that converts raw, unannotated data into instruction tuning datasets.
Jan 1, 2024 Our work Does CLIP Bind Concepts? Probing Compositionality in Large Image Models was accepted to Findings: EACL 2024.
Oct 28, 2023 Attending NeurIPS 2023 to present our work on learning to generate tasks at the Instruction Tuning and Instruction Following Workshop.

selected publications

  1. ArXiv
    Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
    Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach
    ArXiv 2024
  2. Findings
    Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
    Martha Lewis,  Nihal V. Nayak Peilin Yu, Qinan Yu, Jack Merullo,  Stephen H. Bach, and Ellie Pavlick
    In Findings of the Association for Computational Linguistics: EACL 2024 2024
  3. ICLR
    Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
    Nihal V. Nayak Peilin Yu, and Stephen H. Bach
    In International Conference on Learning Representations (ICLR) 2023
  4. TMLR
    Zero-Shot Learning with Common Sense Knowledge Graphs
    Nihal V. Nayak, and Stephen H. Bach
    Transactions on Machine Learning Research 2022
  5. ICLR
    Multitask Prompted Training Enables Zero-Shot Task Generalization
    Victor Sanh, Albert Webson, Colin Raffel,  Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani,  Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush
    In International Conference on Learning Representations (ICLR) 2022