Are NLP Models really able to Solve Simple Math Word Problems? A Patel, S Bhattamishra, N Goyal NAACL 2021, 2021 | 656 | 2021 |
On the ability and limitations of transformers to recognize formal languages S Bhattamishra, K Ahuja, N Goyal EMNLP 2020, 2020 | 142 | 2020 |
Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation A Kumar*, S Bhattamishra*, M Bhandari, P Talukdar NAACL 2019, 3609-3619, 2019 | 136 | 2019 |
On the computational power of transformers and its implications in sequence modeling S Bhattamishra, A Patel, N Goyal CoNLL 2020, 2020 | 70 | 2020 |
Unsung challenges of building and deploying language technologies for low resource language communities P Joshi, C Barnes, S Santy, S Khanuja, S Shah, A Srinivasan, ... arXiv preprint arXiv:1912.03457, 2019 | 46 | 2019 |
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions S Bhattamishra, A Patel, V Kanade, P Blunsom ACL 2023, 2022 | 34 | 2022 |
Understanding in-context learning in transformers and llms by learning to learn discrete functions S Bhattamishra, A Patel, P Blunsom, V Kanade ICLR 2024, 2023 | 33 | 2023 |
Revisiting the Compositional Generalization Abilities of Neural Sequence Models A Patel, S Bhattamishra, P Blunsom, N Goyal ACL 2022, 2022 | 30 | 2022 |
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages S Bhattamishra, K Ahuja, N Goyal COLING 2020, 2020 | 15 | 2020 |
On the Ability of Self-Attention Networks to Recognize Counter Languages S Bhattamishra, K Ahuja, N Goyal Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 10 | 2020 |
Separations in the Representational Capabilities of Transformers and Recurrent Architectures S Bhattamishra, M Hahn, P Blunsom, V Kanade NeurIPS 2024, 2024 | 5 | 2024 |
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations A Patel, S Bhattamishra, S Reddy, D Bahdanau EMNLP 2023, 2023 | 4 | 2023 |
Dynaquant: Compressing deep learning training checkpoints via dynamic quantization A Agrawal, S Reddy, S Bhattamishra, VPS Nookala, V Vashishth, K Rong, ... ACM SoCC 2024, 2023 | 3 | 2023 |
Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization A Agrawal, S Reddy, S Bhattamishra, VPS Nookala, V Vashishth, K Rong, ... Proceedings of the 2024 ACM Symposium on Cloud Computing, 1012-1031, 2024 | | 2024 |
A Formal Framework for Understanding Length Generalization in Transformers X Huang, A Yang, S Bhattamishra, Y Sarrof, A Krebs, H Zhou, P Nakkiran, ... arXiv preprint arXiv:2410.02140, 2024 | | 2024 |