Search Results for author: Nikhil Kandpal

Found 6 papers, 4 papers with code

User Inference Attacks on Large Language Models

no code implementations13 Oct 2023 Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu

Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications.

Large Language Models Struggle to Learn Long-Tail Knowledge

1 code implementation15 Nov 2022 Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel

The Internet contains a wealth of knowledge -- from the birthdays of historical figures to tutorials on how to code -- all of which may be learned by language models.

Entity Linking Question Answering +2

Music Enhancement via Image Translation and Vocoding

no code implementations28 Apr 2022 Nikhil Kandpal, Oriol Nieto, Zeyu Jin

Consumer-grade music recordings such as those captured by mobile devices typically contain distortions in the form of background noise, reverb, and microphone-induced EQ.

Image-to-Image Translation Translation

Deduplicating Training Data Mitigates Privacy Risks in Language Models

3 code implementations14 Feb 2022 Nikhil Kandpal, Eric Wallace, Colin Raffel

Past work has shown that large language models are susceptible to privacy attacks, where adversaries generate sequences from a trained model and detect which sequences are memorized from the training set.

Universal Adversarial Triggers for Attacking and Analyzing NLP

1 code implementation IJCNLP 2019 Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh

We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset.

Language Modelling Reading Comprehension

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