Nitish Joshi

Nitish Joshi

I recently joined Google Deepmind as a Research Scientist to work on Gemini post-training.

I completed my PhD at New York University where I was advised by Prof. He He in the ML2 research group. My research was supported by NSF Graduate Research Fellowship and NYU Dean's Dissertation Fellowship. During PhD, I spent fun summers interning at Google Gemini/Bard and Amazon AWS. Previously, I completed my undergraduate degree in Computer Science at IIT Bombay where I did research with Preethi Jyothi and Mohit Bansal (at UNC Chapel Hill).


Email: joshinh@gmail.com / nitish@nyu.edu


Links: [CV] [Twitter] [Github] [Google Scholar]



Publications

  • Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors
    Chen Yueh-Han, Nitish Joshi, Yulin Chen, Maksym Andriushchenko, Rico Angell, He He
    Preprint, 2025
    [bib] [abstract]

  • Flattery, Fluff, and Fog: Diagnosing and Mitigating Idiosyncratic Biases in Preference Models
    Anirudh Bharadwaj, Chaitanya Malaviya, Nitish Joshi, Mark Yatskar
    Preprint, 2025
    [bib] [abstract]

  • Transformers Struggle to Learn to Search
    Abulhair Saparov, Srushti Pawar, Shreyas Pimpalgaonkar, Nitish Joshi, Richard Yuanzhe Pang, Vishakh Padmakumar, Seyed Mehran Kazemi, Najoung Kim, He He
    ICLR 2025
    [bib] [abstract]

  • LLMs Are Prone to Fallacies in Causal Inference
    Nitish Joshi, Abulhair Saparov, Yixin Wang, He He
    EMNLP 2024
    [bib] [abstract]

  • Personas as a Way to Model Truthfulness in Language Models
    Nitish Joshi*, Javier Rando*, Abulhair Saparov, Najoung Kim, He He
    EMNLP 2024
    [bib] [abstract]

  • Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation
    Aahlad Puli, Nitish Joshi, Yoav Wald, He He, and Rajesh Ranganath
    TMLR, 2024
    [bib] [abstract]

  • Improving Multi-Hop Reasoning in LLMs by Learning from Rich Human Feedback
    Nitish Joshi, Koushik Kalyanaraman, Zhiting Hu, Kumar Chellapilla, He He, Li Erran Li
    NucLeaR Workshop, AAAI 2024
    [bib] [abstract]

  • Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
    Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim*, He He*
    NeurIPS 2023
    [bib] [abstract]

  • Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations
    Chenglei Si*, Dan Friedman*, Nitish Joshi, Shi Feng, Danqi Chen, He He
    ACL 2023
    [bib] [abstract]

  • Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens
    Nitish Joshi*, Xiang Pan* and He He
    EMNLP 2022
    [bib] [abstract]

  • 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, Samuel R. Bowman
    NAACL 2022
    [bib] [abstract]

  • An Investigation of the (In)effectiveness of Counterfactually Augmented Data
    Nitish Joshi, and He He.
    ACL 2022
    [bib] [abstract]

  • Coupled Training of Sequence-to-Sequence Models for Accented Speech Recognition
    Vinit Unni*, Nitish Joshi*, and Preethi Jyothi.
    ICASSP 2020
    [bib] [abstract]

  • Explore, Propose and Assemble: An Interpretable Model for Multi-hop Reading Comprehension
    Yichen Jiang*, Nitish Joshi*, Yen-Chun Chen and Mohit Bansal
    ACL 2019
    [bib] [abstract] [code]

  • Cross-lingual Training for Automatic Question Generation
    Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, and Preethi Jyothi.
    ACL 2019
    [bib] [abstract] [dataset]


Miscellany