Summary of Demystifying Verbatim Memorization in Large Language Models, by Jing Huang et al.
Demystifying Verbatim Memorization in Large Language Models
by Jing Huang, Diyi Yang, Christopher Potts
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework develops a controlled setting for studying Large Language Models’ (LLMs) verbatim memorization by continuing pre-training from Pythia checkpoints. It finds that non-trivial amounts of repetition are necessary, later checkpoints are more likely to memorize sequences, and distributed model states trigger the generation of memorized sequences using general language modeling capabilities. The framework also evaluates unlearning methods, which often fail to remove verbatim memorized information while degrading the LLM’s quality. This research challenges the hypothesis that verbatim memorization stems from specific model weights or mechanisms, instead suggesting it is intertwined with the LLM’s general capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) can remember long sequences of text word-for-word. This can have serious consequences for privacy and legal issues. Researchers tried to understand why this happens using data they observed. To add more insight, scientists developed a way to test how well LLMs memorize sequences in a controlled environment. They found that it takes a certain amount of repetition for the model to remember something verbatim. The best models are better at remembering sequences, even if they’re not part of what they were trained on. The team also looked into ways to “unlearn” this memorization and found that these methods often don’t work well. |
