I'm a researcher on the Aristo team at the Allen Institute for AI (AI2) in Seattle.
Broadly, I am interested in building general-purpose AI agents that can:
(1) reason about complex problems through natural language rationalization,
(2) faithfully express their uncertainty in their knowledge and reasoning, and
(3) continuously improve through self-reflection and human feedback.
My current research focus is on AI for Scientific Discovery. I am particularly interested in AI for Math (e.g., Automated Theorem Proving, better foundation models for Math),
building agents for finding supporting/contrary evidence from literature, and retrieval-augmented and memory architectures for supporting these use cases.
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Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande, Ashwin Kalyan, Peter Clark, Ashish Sabharwal, Tushar Khot
ICLR 2024
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Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, [...], Shashank Gupta, [...], Peter Clark
NeurIPS 2023
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Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners
Shashank Gupta, Subhabrata Mukherjee, Krishan Subudhi, Eduardo Gonzalez, Damien Jose, Ahmed H. Awadallah, Jianfeng Gao
Arxiv 2022
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Knowledge Infused Decoding
Ruibo Liu, Guoqing Zheng, Shashank Gupta, Radhika Gaonkar, Chongyang Gao, Soroush Vosoughi, Milad Shokouhi, Ahmed Hassan Awadallah
ICLR 2022
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Exploring Low-Cost Transformer Model Compression for Large-Scale Commercial Reply Suggestions
Vaishnavi Shrivastava*, Radhika Gaonkar*, Shashank Gupta*, Abhishek Jha
*equal contribution
Arxiv 2021
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CogCompNLP: Your Swiss Army Knife for NLP
Daniel Khashabi, Mark Sammons, [...], Shashank Gupta, [...], Dan Roth
LREC 2018
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Web-scale entity annotation using MapReduce
Shashank Gupta, Varun Chandramouli, Soumen Chakrabarti
HiPC 2013
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