Universal Adversarial Triggers for Attacking and Analyzing NLP. - Eric Wallace; Shi Feng 005; Nikhil Kandpal; Matt Gardner 001; Sameer Singh 001 (EMNLP/IJCNLP (1), 2019)

Universal Adversarial Triggers for NLP. - Eric Wallace; Shi Feng 005; Nikhil Kandpal; Matt Gardner 001; Sameer Singh 001 (CoRR, 2019)

Music Enhancement via Image Translation and Vocoding. - Nikhil Kandpal; Oriol Nieto; Zeyu Jin (ICASSP, 2022)

Deduplicating Training Data Mitigates Privacy Risks in Language Models. - Nikhil Kandpal; Eric Wallace; Colin Raffel (ICML, 2022)

Large Language Models Struggle to Learn Long-Tail Knowledge. - Nikhil Kandpal; Haikang Deng; Adam Roberts; Eric Wallace; Colin Raffel (ICML, 2023)

Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. - Nikhil Kandpal; Brian Lester; Mohammed Muqeeth; Anisha Mascarenhas; Monty Evans; Vishal Baskaran; Tenghao Huang; Haokun Liu; Colin Raffel (ICML, 2023)

Backdoor Attacks for In-Context Learning with Language Models. - Nikhil Kandpal; Matthew Jagielski; Florian Tramèr; Nicholas Carlini (CoRR, 2023)

User Inference Attacks on Large Language Models. - Nikhil Kandpal; Krishna Pillutla; Alina Oprea; Peter Kairouz; Christopher A. Choquette-Choo; Zheng Xu 002 (EMNLP, 2024)

AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution. - Fengyuan Liu; Nikhil Kandpal; Colin Raffel (ICLR, 2025)

Efficient Model Development through Fine-tuning Transfer. - Pin-Jie Lin; Rishab Balasubramanian; Fengyuan Liu; Nikhil Kandpal; Tu Vu (CoRR, 2025)

Position: The Most Expensive Part of an LLM should be its Training Data. - Nikhil Kandpal; Colin Raffel (CoRR, 2025)

Enhancing Training Data Attribution with Representational Optimization. - Weiwei Sun; Haokun Liu; Nikhil Kandpal; Colin Raffel; Yiming Yang (CoRR, 2025)

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text. - Nikhil Kandpal; Brian Lester; Colin Raffel; Sebastian Majstorovic; Stella Biderman; Baber Abbasi; Luca Soldaini; Enrico Shippole; A. Feder Cooper; Aviya Skowron; John Kirchenbauer; Shayne Longpre; et al. (CoRR, 2025)