Nikhil Kandpal

According to our database, Nikhil Kandpal authored at least 13 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, Lintang Sutawika, Alon Albalak, Zhenlin Xu, Guilherme Penedo, Loubna Ben Allal, Elie Bakouch, John David Pressman, Honglu Fan, Dashiell Stander, Guangyu Song, Aaron Gokaslan, Tom Goldstein, Brian R. Bartoldson, Bhavya Kailkhura, Tyler Murray

CoRR, June, 2025

Enhancing Training Data Attribution with Representational Optimization.

Weiwei Sun, Haokun Liu, Nikhil Kandpal, Colin Raffel, Yiming Yang

CoRR, May, 2025

Position: The Most Expensive Part of an LLM should be its Training Data.

Nikhil Kandpal, Colin Raffel

CoRR, April, 2025

Efficient Model Development through Fine-tuning Transfer.

Pin-Jie Lin, Rishab Balasubramanian, Fengyuan Liu, Nikhil Kandpal, Tu Vu

CoRR, March, 2025

AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution.

Fengyuan Liu, Nikhil Kandpal, Colin Raffel

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

User Inference Attacks on Large Language Models.

Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

Backdoor Attacks for In-Context Learning with Language Models.

Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini

CoRR, 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

Proceedings of the International Conference on Machine Learning, 2023

Large Language Models Struggle to Learn Long-Tail Knowledge.

Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel

Proceedings of the International Conference on Machine Learning, 2023

2022

Deduplicating Training Data Mitigates Privacy Risks in Language Models.

Nikhil Kandpal, Eric Wallace, Colin Raffel

Proceedings of the International Conference on Machine Learning, 2022

Music Enhancement via Image Translation and Vocoding.

Nikhil Kandpal, Oriol Nieto, Zeyu Jin

Proceedings of the IEEE International Conference on Acoustics, 2022

2019

Universal Adversarial Triggers for NLP.

Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh

CoRR, 2019

Universal Adversarial Triggers for Attacking and Analyzing NLP.

Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019