Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation. - Ashutosh Kumar; Satwik Bhattamishra; Manik Bhandari; Partha P. Talukdar (NAACL-HLT (1), 2019)

Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities. - Pratik Joshi; Christain Barnes; Sebastin Santy; Simran Khanuja; Sanket Shah; Anirudh Srinivasan; Satwik Bhattamishra; Sunayana Sitaram; Monojit Choudhury; Kalika Bali (CoRR, 2019)

On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. - Satwik Bhattamishra; Kabir Ahuja; Navin Goyal (COLING, 2020)

On the Computational Power of Transformers and Its Implications in Sequence Modeling. - Satwik Bhattamishra; Arkil Patel; Navin Goyal (CoNLL, 2020)

On the Ability and Limitations of Transformers to Recognize Formal Languages. - Satwik Bhattamishra; Kabir Ahuja; Navin Goyal (EMNLP (1), 2020)

On the Ability of Self-Attention Networks to Recognize Counter Languages. - Satwik Bhattamishra; Kabir Ahuja; Navin Goyal (CoRR, 2020)

Are NLP Models really able to Solve Simple Math Word Problems? - Arkil Patel; Satwik Bhattamishra; Navin Goyal (NAACL-HLT, 2021)

Revisiting the Compositional Generalization Abilities of Neural Sequence Models. - Arkil Patel; Satwik Bhattamishra; Phil Blunsom; Navin Goyal (ACL (2), 2022)

Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions. - Satwik Bhattamishra; Arkil Patel; Varun Kanade; Phil Blunsom (ACL (1), 2023)

MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations. - Arkil Patel; Satwik Bhattamishra; Siva Reddy; Dzmitry Bahdanau (EMNLP, 2023)

DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. - Amey Agrawal; Sameer Reddy; Satwik Bhattamishra; Venkata Prabhakara Sarath Nookala; Vidushi Vashishth; Kexin Rong 001; Alexey Tumanov (CoRR, 2023)

Structural Transfer Learning in NL-to-Bash Semantic Parsers. - Kyle Duffy; Satwik Bhattamishra; Phil Blunsom (CoRR, 2023)

Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. - Amey Agrawal; Sameer Reddy; Satwik Bhattamishra; Venkata Prabhakara Sarath Nookala; Vidushi Vashishth; Kexin Rong 001; Alexey Tumanov (SoCC, 2024)

Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions. - Satwik Bhattamishra; Arkil Patel; Phil Blunsom; Varun Kanade (ICLR, 2024)

Separations in the Representational Capabilities of Transformers and Recurrent Architectures. - Satwik Bhattamishra; Michael Hahn 001; Phil Blunsom; Varun Kanade (NeurIPS, 2024)

A Formal Framework for Understanding Length Generalization in Transformers. - Xinting Huang; Andy Yang; Satwik Bhattamishra; Yash Raj Sarrof; Andreas Krebs; Hattie Zhou; Preetum Nakkiran; Michael Hahn 001 (ICLR, 2025)