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ORCIDNitish Joshi
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2020 – today
- 2024
[j2]Aahlad Manas Puli, Nitish Joshi, Yoav Wald, He He, Rajesh Ranganath:
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. Trans. Mach. Learn. Res. 2024 (2024)- [i10]Nitish Joshi, Abulhair Saparov, Yixin Wang, He He:
LLMs Are Prone to Fallacies in Causal Inference. CoRR abs/2406.12158 (2024) - 2023
- [c8]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. ACL (1) 2023: 11289-11310 - [c7]Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Mehran Kazemi, Najoung Kim, He He:
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. NeurIPS 2023 - [i9]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. CoRR abs/2305.13299 (2023) - [i8]Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim, He He:
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. CoRR abs/2305.15269 (2023) - [i7]Nitish Joshi, Javier Rando, Abulhair Saparov, Najoung Kim, He He:
Personas as a Way to Model Truthfulness in Language Models. CoRR abs/2310.18168 (2023) - 2022
- [c6]Nitish Joshi, He He:
An Investigation of the (In)effectiveness of Counterfactually Augmented Data. ACL (1) 2022: 3668-3681 - [c5]Nitish Joshi, Xiang Pan, He He:
Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. EMNLP 2022: 9804-9817 - [c4]Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel R. Bowman:
QuALITY: Question Answering with Long Input Texts, Yes! NAACL-HLT 2022: 5336-5358 - [i6]Aahlad Manas Puli, Nitish Joshi, He He, Rajesh Ranganath:
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. CoRR abs/2210.01302 (2022) - [i5]Nitish Joshi, Xiang Pan, He He:
Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. CoRR abs/2210.14011 (2022) - 2021
- [j1]Shubham Dewangan
, Shreya Alva, Nitish Joshi, Pushpak Bhattacharyya:
Experience of neural machine translation between Indian languages. Mach. Transl. 35(1): 71-99 (2021) - [i4]Nitish Joshi, He He:
An Investigation of the (In)effectiveness of Counterfactually Augmented Data. CoRR abs/2107.00753 (2021) - [i3]Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel R. Bowman:
QuALITY: Question Answering with Long Input Texts, Yes! CoRR abs/2112.08608 (2021) - 2020
- [c3]Vinit Unni, Nitish Joshi, Preethi Jyothi:
Coupled Training of Sequence-to-Sequence Models for Accented Speech Recognition. ICASSP 2020: 8254-8258
2010 – 2019
- 2019
- [c2]Yichen Jiang, Nitish Joshi, Yen-Chun Chen, Mohit Bansal:
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension. ACL (1) 2019: 2714-2725 - [c1]Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi:
Cross-Lingual Training for Automatic Question Generation. ACL (1) 2019: 4863-4872 - [i2]Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi:
Cross-Lingual Training for Automatic Question Generation. CoRR abs/1906.02525 (2019) - [i1]Yichen Jiang, Nitish Joshi, Yen-Chun Chen, Mohit Bansal:
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension. CoRR abs/1906.05210 (2019)
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last updated on 2024-08-10 00:31 CEST by the dblp team
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