| 2025 | ICLR | 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 |
| 2024 | CLOUD | Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. | Amey Agrawal, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov |
| 2024 | ICLR | Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions. | Satwik Bhattamishra, Arkil Patel, Phil Blunsom, Varun Kanade |
| 2023 | ACL | Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions. | Satwik Bhattamishra, Arkil Patel, Varun Kanade, Phil Blunsom |
| 2023 | EMNLP | MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations. | Arkil Patel, Satwik Bhattamishra, Siva Reddy, Dzmitry Bahdanau |
| 2022 | ACL | Revisiting the Compositional Generalization Abilities of Neural Sequence Models. | Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal |
| 2021 | NAACL | Are NLP Models really able to Solve Simple Math Word Problems? | Arkil Patel, Satwik Bhattamishra, Navin Goyal |
| 2020 | COLING | On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. | Satwik Bhattamishra, Kabir Ahuja, Navin Goyal |
| 2020 | CoNLL | On the Computational Power of Transformers and Its Implications in Sequence Modeling. | Satwik Bhattamishra, Arkil Patel, Navin Goyal |
| 2020 | EMNLP | On the Ability and Limitations of Transformers to Recognize Formal Languages. | Satwik Bhattamishra, Kabir Ahuja, Navin Goyal |
| 2019 | NAACL | Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation. | Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, Partha P. Talukdar |