author
Ph.D. candidate
,
Brown University
Nihal V. Nayak is a fifth-year Ph.D. student in the Department of Computer Science at Brown University, where he is advised by Stephen H. Bach. His research focuses on zero-shot generalization in deep neural networks and, more broadly, on learning with limited labeled data. His work has been published in leading machine learning conferences and journals, including ICLR, TMLR, ACL Demo, EACL Findings, and MLSys.
The latest from Nihal
Blog
How Bonito helps fine-tune specialized LLMs faster than ever
Fine-tuning specialized LLMs demands a lot of time and cost We developed Bonito to make this process faster, cheaper, and easier.
Learning to Generate Instructions to Adapt Language Models to New Tasks
We present Bonito, the first open-source model for conditional task generation: the problem of converting unannotated corpus into a collection of tasks for instruction tuning. Our goal is to enable efficient task adaptation of instruction tuned language models on users' specialized, private data without relying on proprietary API-access-only models like GPT-4. We create Bonito by remixing existing, general-purpose instruction tuning data into a new training mixture for conditional task generation. Bonito learns to generate new tasks conditioned on the text and desired task type. The generated instructions in the specialized domain can be used to further train language models. We...
Research Paper
Learning to Generate Instructions to Adapt Language Models to New Tasks
We present Bonito, the first open-source model for conditional task generation: the problem of converting unannotated corpus into a collection of tasks for instruction tuning. Our goal is to enable efficient task adaptation of instruction tuned language models on users’ specialized, private data without relying on proprietary API-access-only models like GPT-4. We create Bonito by remixing existing, general-purpose instruction tuning…
Zero-Shot Learning with Common Sense Knowledge Graphs
Zero-shot learning with Common Sense Knowledge Graphs is a general-purpose framework with a novel transformer graph convolutional network for generating class representations from common sense knowledge graphs, which improves over existing WordNet-based methods on zero-shot learning tasks.
Research Paper
Zero-Shot Learning with Common Sense Knowledge Graphs
Zero-shot learning with Common Sense Knowledge Graphs is a general-purpose framework with a novel transformer graph convolutional network for generating class representations from common sense knowledge graphs, which improves over existing WordNet-based methods on zero-shot learning tasks.
