December 2020

Most likely not.

Yet, OpenAI’s GPT-2 language model does know how to reach a certain Peter W— (name redacted for privacy). When prompted with a short snippet of Internet text, the model accurately generates Peter’s contact information, including his work address, email, phone, and fax:

In our recent paper, we evaluate how large language models memorize and regurgitate such rare snippets of their training data. We focus on GPT-2 and find that at least 0.1% of its text generations (a very conservative estimate) contain long verbatim strings that are “copy-pasted” from a document in its training set.

Such memorization would be an obvious issue for language models that are trained on private data, e.g., on users’ emails, as the model might inadvertently output a user’s sensitive conversations. Yet, even for models that are trained on public data from the Web (e.g., GPT-2, GPT-3, T5, RoBERTa, TuringNLG), memorization of training data raises multiple challenging regulatory questions, ranging from misuse of personally identifiable information to copyright infringement.

Read More »Does GPT-2 Know Your Phone Number?

Deep reinforcement learning has made significant progress in the last few years, with success stories in robotic control, game playing and science problems. While RL methods present a general paradigm where an agent learns from its own interaction with an environment, this requirement for “active” data collection is also a major hindrance in the application of RL methods to real-world problems, since active data collection is often expensive and potentially unsafe. An alternative “data-driven” paradigm of RL, referred to as offline RL (or batch RL) has recently regained popularity as a viable path towards effective real-world RL. As shown in the figure below, offline RL requires learning skills solely from previously collected datasets, without any active environment interaction. It provides a way to utilize previously collected datasets from a variety of sources, including human demonstrations, prior experiments, domain-specific solutions and even data from different but related problems, to build complex decision-making engines.

Read More »Offline Reinforcement Learning: How Conservative Algorithms Can Enable New Applications