Nihal Nayak
Update: I’m on the job market! Reach out if you think I’d be a good fit in your organization.
CV (Updated in July 2024)
I am a Ph.D. candidate in Computer Science at Brown University. I work with Stephen Bach on zero-shot generalization in deep neural networks and, more broadly, on learning with limited labeled data.
Here is a summary of my recent research:
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Synthetic Data Generation. Introduced Bonito, an open-source model that converts unannotated text from specialized domains into instruction tuning datasets to adapt large language models without annotations (ACL Findings, 24).
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Compositionality. Introduced compositional prompt tuning, a new parameter-efficient prompt learning method that learns to decompose classes into sub-concepts and recomposes them at test time for improved zero-shot performance (ICLR, 23). Next, created a synthetic benchmark to systematically study compositionality in vision language models. We find that they often fail to generalize to compositions that require binding (EACL Findings, 24).
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Structured Knowledge. Created ZSL-KG, a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs (TMLR, 22).
Email : nnayak2 [at] cs [dot] brown [dot] edu
news
| Jun 5, 2024 | Excited to share that Bonito was accepted to ACL Findings 2024. |
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| May 3, 2024 | Spotlight talk at NeNLP and invited talks at Snorkel (Video) and Amy Greenwald’s Reseach Group on Bonito. |
| 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. |