Revealed: The Authors Whose Pirated Books Are Powering Generative AI - The Atlantic
Updated at 1:40 p.m. ET on September 25, 2023<br>Editor’s note: This article is part of The Atlantic’s series on Books3. Check out our searchable Books3 database to find specific authors and titles. A deeper analysis of what is in the database is here.<br>One of the most troubling issues around generative AI is simple: It’s being made in secret. To produce humanlike answers to questions, systems such as ChatGPT process huge quantities of written material. But few people outside of companies such as Meta and OpenAI know the full extent of the texts these programs have been trained on.
Some training text comes from Wikipedia and other online writing, but high-quality generative AI requires higher-quality input than is usually found on the internet—that is, it requires the kind found in books. In a lawsuit filed in California last month, the writers Sarah Silverman, Richard Kadrey, and Christopher Golden allege that Meta violated copyright laws by using their books to train LLaMA, a large language model similar to OpenAI’s GPT-4—an algorithm that can generate text by mimicking the word patterns it finds in sample texts. But neither the lawsuit itself nor the commentary surrounding it has offered a look under the hood: We have not previously known for certain whether LLaMA was trained on Silverman’s, Kadrey’s, or Golden’s books, or any others, for that matter.
In fact, it was. I recently obtained and analyzed a dataset used by Meta to train LLaMA. Its contents more than justify a fundamental aspect of the authors’ allegations: Pirated books are being used as inputs for computer programs that are changing how we read, learn, and communicate. The future promised by AI is written with stolen words.
Upwards of 170,000 books, the majority published in the past 20 years, are in LLaMA’s training data. In addition to work by Silverman, Kadrey, and Golden, nonfiction by Michael Pollan, Rebecca Solnit, and Jon Krakauer is being used, as are thrillers by James Patterson and Stephen King and other fiction by George Saunders, Zadie Smith, and Junot Díaz. These books are part of a dataset called “Books3,” and its use has not been limited to LLaMA. Books3 was also used to train Bloomberg’s BloombergGPT, EleutherAI’s GPT-J—a popular open-source model—and likely other generative-AI programs now embedded in websites across the internet. A Meta spokesperson declined to comment on the company’s use of Books3; a spokesperson for Bloomberg confirmed via email that Books3 was used to train the initial model of BloombergGPT and added, “We will not include the Books3 dataset among the data sources used to train future versions of BloombergGPT”; and Stella Biderman, EleutherAI’s executive director, did not dispute that the company used Books3 in GPT-J’s training data.
As a writer and computer programmer, I’ve been curious about what kinds of books are used to train generative-AI systems. Earlier this summer, I began reading online discussions among academic and hobbyist AI developers on sites such as GitHub and Hugging Face. These eventually led me to a direct download of “the Pile,” a massive cache of training text created by EleutherAI that contains the Books3 dataset, plus material from a variety of other sources: YouTube-video subtitles, documents and transcriptions from the European Parliament, English Wikipedia, emails sent and received by Enron Corporation employees before its 2001 collapse, and a lot more. The variety is not entirely surprising. Generative AI works by analyzing the relationships among words in intelligent-sounding language, and given the complexity of these relationships, the subject matter is typically less important than the sheer quantity of text. That’s why The-Eye.eu, a site that hosted the Pile until recently—it received a takedown notice from a Danish anti-piracy group—says its purpose is “to suck up and serve large datasets.”
The Pile is too large to be opened in a text-editing application, so I wrote a series of programs to manage it. I first extracted all the lines labeled “Books3” to isolate the Books3 dataset. Here’s a sample from the resulting dataset:
{"text": "\n\nThis book is a work of fiction. Names, characters, places and incidents are products of the authors' imagination or are used fictitiously. Any resemblance to actual events or locales or persons, living or dead, is entirely coincidental.\n\n | POCKET BOOKS, a division of Simon & Schuster Inc. \n1230 Avenue of the Americas, New York, NY 10020 \nwww.SimonandSchuster.com\n\n---|---
This is the beginning of a line that, like all lines in the dataset, continues for many thousands of words and contains the complete text of a book. But what book? There were no explicit labels with titles, author names, or metadata. Just the label “text,” which reduced the books to the function they serve for AI training. To identify the entries, I wrote another program to...