Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4

Kent K. Chang, Mackenzie Hanh Cramer, Sandeep Soni, David Bamman

Submission Type: Regular Long Paper

Submission Track: Computational Social Science and Cultural Analytics

Keywords: membership inference, memorization, cultural analytics, large language models

Abstract: In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query. We find that OpenAI models have memorized a wide collection of copyrighted materials, and that the degree of memorization is tied to the frequency with which passages of those books appear on the web. The ability of these models to memorize an unknown set of books complicates assessments of measurement validity for cultural analytics by contaminating test data; we show that models perform much better on memorized books than on non-memorized books for downstream tasks. We argue that this supports a case for open models whose training data is known.

Submission Number: 650