| QuALITY: Question Answering with Long Input Texts, Yes! RY Pang, A Parrish, N Joshi, N Nangia, J Phang, A Chen, V Padmakumar, ... arXiv preprint arXiv:2112.08608, 2021 | 213 | 2021 |
| Testing the general deductive reasoning capacity of large language models using ood examples A Saparov, RY Pang, V Padmakumar, N Joshi, M Kazemi, N Kim, H He Advances in Neural Information Processing Systems 36, 3083-3105, 2023 | 122 | 2023 |
| Cross-Lingual Training for Automatic Question Generation V Kumar, N Joshi, A Mukherjee, G Ramakrishnan, P Jyothi arXiv preprint arXiv:1906.02525, 2019 | 80 | 2019 |
| Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension Y Jiang, N Joshi, YC Chen, M Bansal arXiv preprint arXiv:1906.05210, 2019 | 62 | 2019 |
| An Investigation of the (In) effectiveness of Counterfactually Augmented Data N Joshi, H He arXiv preprint arXiv:2107.00753, 2021 | 59 | 2021 |
| Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations C Si, D Friedman, N Joshi, S Feng, D Chen, H He arXiv preprint arXiv:2305.13299, 2023 | 57 | 2023 |
| Personas as a Way to Model Truthfulness in Language Models N Joshi, J Rando, A Saparov, N Kim, H He arXiv preprint arXiv:2310.18168, 2023 | 45 | 2023 |
| Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens N Joshi, X Pan, H He arXiv preprint arXiv:2210.14011, 2022 | 43 | 2022 |
| LLMs Are Prone to Fallacies in Causal Inference N Joshi, A Saparov, Y Wang, H He arXiv preprint arXiv:2406.12158, 2024 | 34 | 2024 |
| Nuisances via negativa: Adjusting for spurious correlations via data augmentation A Puli, N Joshi, Y Wald, H He, R Ranganath arXiv preprint arXiv:2210.01302, 2022 | 20 | 2022 |
| Transformers Struggle to Learn to Search A Saparov, S Pawar, S Pimpalgaonkar, N Joshi, RY Pang, ... arXiv preprint arXiv:2412.04703, 2024 | 15 | 2024 |
| Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors C Yueh-Han, N Joshi, Y Chen, M Andriushchenko, R Angell, H He arXiv preprint arXiv:2506.10949, 2025 | 6* | 2025 |
| Improving Multi-Hop Reasoning in LLMs by Learning from Rich Human Feedback N Joshi, K Kalyanaraman, Z Hu, K Chellapilla, H He, LE Li Neuro-Symbolic Learning and Reasoning in the era of Large Language Models, 2023 | 5 | 2023 |
| Coupled training of sequence-to-sequence models for accented speech recognition V Unni, N Joshi, P Jyothi ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 5 | 2020 |
| Flattery, Fluff, and Fog: Diagnosing and Mitigating Idiosyncratic Biases in Preference Models A Bharadwaj, C Malaviya, N Joshi, M Yatskar arXiv preprint arXiv:2506.05339, 2025 | 2 | 2025 |
| Is It Thinking or Cheating? Detecting Implicit Reward Hacking by Measuring Reasoning Effort X Wang, N Joshi, B Plank, R Angell, H He arXiv preprint arXiv:2510.01367, 2025 | 1 | 2025 |
| Understanding and Mitigating Goal Misgeneralization in Language Models N Joshi New York University, 2025 | | 2025 |
| LLM-Cite: Cheap Fact Verification with Attribution via URL Generation N Joshi, A Taly, D Muppalla | | |
| Transformers Struggle to Learn to Search Without In-context Exploration A Saparov, SA Pawar, S Pimpalgaonkar, N Joshi, RY Pang, ... The Thirteenth International Conference on Learning Representations, 0 | | |
He HeNew York UniversityVerified email at cs.nyu.edu
