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In this work, we carry out a data archaeology to infer books that are knownto ChatGPT and GPT-4 using a name cloze membership inference query. We findthat OpenAI models have memorized a wide collection of copyrighted materials,and that the degree of memorization is tied to the frequency with whichpassages of those books appear on the web. The ability of these models tomemorize an unknown set of books complicates assessments of measurementvalidity for cultural analytics by contaminating test data; we show that modelsperform much better on memorized books than on non-memorized books fordownstream tasks. We argue that this supports a case for open models whosetraining data is known.

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