Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension. - Yichen Jiang; Nitish Joshi; Yen-Chun Chen 001; Mohit Bansal (ACL (1), 2019)

Cross-Lingual Training for Automatic Question Generation. - Vishwajeet Kumar; Nitish Joshi; Arijit Mukherjee; Ganesh Ramakrishnan; Preethi Jyothi (ACL (1), 2019)

Coupled Training of Sequence-to-Sequence Models for Accented Speech Recognition. - Vinit Unni; Nitish Joshi; Preethi Jyothi (ICASSP, 2020)

Experience of neural machine translation between Indian languages. - Shubham Dewangan; Shreya Alva; Nitish Joshi; Pushpak Bhattacharyya (Mach. Transl., 2021)

An Investigation of the (In)effectiveness of Counterfactually Augmented Data. - Nitish Joshi; He He 001 (ACL (1), 2022)

Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. - Nitish Joshi; Xiang Pan 001; He He 001 (EMNLP, 2022)

QuALITY: Question Answering with Long Input Texts, Yes! - Richard Yuanzhe Pang; Alicia Parrish; Nitish Joshi; Nikita Nangia; Jason Phang; Angelica Chen; Vishakh Padmakumar; Johnny Ma; Jana Thompson; He He 001; Samuel R. Bowman (NAACL-HLT, 2022)

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. - Aahlad Manas Puli; Nitish Joshi; He He 001; Rajesh Ranganath (CoRR, 2022)

Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. - Chenglei Si; Dan Friedman; Nitish Joshi; Shi Feng 005; Danqi Chen 001; He He 001 (ACL (1), 2023)

Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. - Abulhair Saparov; Richard Yuanzhe Pang; Vishakh Padmakumar; Nitish Joshi; Mehran Kazemi; Najoung Kim; He He 001 (NeurIPS, 2023)

Personas as a Way to Model Truthfulness in Language Models. - Nitish Joshi; Javier Rando; Abulhair Saparov; Najoung Kim; He He 001 (EMNLP, 2024)

LLMs Are Prone to Fallacies in Causal Inference. - Nitish Joshi; Abulhair Saparov; Yixin Wang; He He 001 (EMNLP, 2024)

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. - Aahlad Manas Puli; Nitish Joshi; Yoav Wald; He He 001; Rajesh Ranganath (Trans. Mach. Learn. Res., 2024)

Transformers Struggle to Learn to Search. - Abulhair Saparov; Srushti Ajay Pawar; Shreyas Pimpalgaonkar; Nitish Joshi; Richard Yuanzhe Pang; Vishakh Padmakumar; Mehran Kazemi; Najoung Kim; He He 001 (ICLR, 2025)

Flattery, Fluff, and Fog: Diagnosing and Mitigating Idiosyncratic Biases in Preference Models. - Anirudh Bharadwaj; Chaitanya Malaviya; Nitish Joshi; Mark Yatskar (CoRR, 2025)

Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors. - Chen Yueh-Han; Nitish Joshi; Yulin Chen; Maksym Andriushchenko; Rico Angell; He He 001 (CoRR, 2025)