Papers by A. Feder Cooper
Measuring memorization in language models via probabilistic extraction (2025.naacl-long)
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Jamie Hayes, Marika Swanberg, Harsh Chaudhari, Itay Yona, Ilia Shumailov, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, A. Feder Cooper
| Challenge: | Large language models (LLMs) are susceptible to memorizing training data, raising concerns about the potential extraction of sensitive information at generation time. |
| Approach: | They propose a method that splits training example into prefix and suffix, prompts LLM with suffix and deems it extractable if it generates the suffix using greedy sampling. |
| Outcome: | The proposed method is unreliable because it does not account for non-determinism in more realistic sampling schemes. |