Daphne Ippolito

A Recipe For Arbitrary Text Style Transfer with Large Language Models

2021 · arXiv (Cornell University)

In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer. We present a prompting method that we call augmented zero-shot learning, which frames style transfer as a sentence rewriting task …

Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy

2022 · arXiv (Cornell University)

Studying data memorization in neural language models helps us understand the risks (e.g., to privacy or copyright) associated with models regurgitating training data and aids in the development of countermeasures. Many prior works -- and …

Deduplicating Training Data Makes Language Models Better

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Quantifying Memorization Across Neural Language Models

2022 · arXiv (Cornell University)

Large language models (LMs) have been shown to memorize parts of their training data, and when prompted appropriately, they will emit the memorized training data verbatim. This is undesirable because memorization violates privacy (exposing user …

PaLM: Scaling Language Modeling with Pathways

2022 · arXiv (Cornell University)

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to …