Nihal Nayak

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 27, 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.
Dec 31, 2023	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

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

Bib PDF Code Project

@article{bonito:arxiv24,
  author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.},
  title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
  volume = {arXiv:2402.18334 [cs.CL]},
  year = {2024},
  abbr = {ArXiv},
  selected = {true},
  bibtex_show = {true},
  preprint = {true},
  pdf = {https://arxiv.org/abs/2402.18334},
  code = {https://github.com/BatsResearch/nayak-arxiv24-code},
  project = {https://github.com/BatsResearch/bonito}
}

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

Bib PDF Code

@inproceedings{lewis:eacl24,
  title = {Does CLIP Bind Concepts? Probing Compositionality in Large Image Models},
  author = {Lewis, Martha and Nayak, Nihal V. and Yu, Peilin and Yu, Qinan and Merullo, Jack and Bach, Stephen H. and Pavlick, Ellie},
  year = {2024},
  selected = {true},
  preprint = {false},
  bibtex_show = {true},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2024},
  abbr = {Findings},
  pdf = {https://arxiv.org/abs/2212.10537},
  code = {https://github.com/marthaflinderslewis/clip-binding}
}

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

Bib PDF Code

@inproceedings{csp:iclr23,
  author = {Nayak, Nihal V. and Yu, Peilin and Bach, Stephen H.},
  title = {Learning to Compose Soft Prompts for Compositional Zero-Shot Learning},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  abbr = {ICLR},
  bibtex_show = {true},
  selected = {true},
  pdf = {https://arxiv.org/abs/2204.03574},
  code = {https://github.com/BatsResearch/csp}
}

TMLR

Zero-Shot Learning with Common Sense Knowledge Graphs

Nihal V. Nayak, and Stephen H. Bach

Transactions on Machine Learning Research 2022

Bib PDF Code Project

@article{nayak:tmlr22,
  author = {Nayak, Nihal V. and Bach, Stephen H.},
  title = {Zero-Shot Learning with Common Sense Knowledge Graphs},
  year = {2022},
  pdf = {https://arxiv.org/abs/2006.10713},
  project = {https://github.com/BatsResearch/zsl-kg},
  code = {https://github.com/BatsResearch/nayak-arxiv20-code},
  journal = {Transactions on Machine Learning Research},
  abbr = {TMLR},
  bibtex_show = {true},
  selected = {true}
}

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

Bib PDF Code Project

@inproceedings{sanh:iclr22,
  author = {Sanh, Victor and Webson, Albert and Raffel, Colin and Bach, Stephen H. and Sutawika, Lintang and Alyafeai, Zaid and Chaffin, Antoine and Stiegler, Arnaud and Scao, Teven Le and Raja, Arun and Dey, Manan and Bari, M Saiful and Xu, Canwen and Thakker, Urmish and Sharma, Shanya Sharma and Szczechla, Eliza and Kim, Taewoon and Chhablani, Gunjan and Nayak, Nihal V. and Datta, Debajyoti and Chang, Jonathan and Jiang, Mike Tian-Jian and Wang, Han and Manica, Matteo and Shen, Sheng and Yong, Zheng Xin and Pandey, Harshit and Bawden, Rachel and Wang, Thomas and Neeraj, Trishala and Rozen, Jos and Sharma, Abheesht and Santilli, Andrea and Fevry, Thibault and Fries, Jason Alan and Teehan, Ryan and Biderman, Stella and Gao, Leo and Bers, Tali and Wolf, Thomas and Rush, Alexander M.},
  title = {Multitask Prompted Training Enables Zero-Shot Task Generalization},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  pdf = {https://arxiv.org/abs/2110.08207},
  project = {https://github.com/bigscience-workshop/promptsource},
  code = {https://github.com/bigscience-workshop/t-zero},
  abbr = {ICLR},
  bibtex_show = {true},
  selected = {true}
}