Summary of Measuring Non-adversarial Reproduction Of Training Data in Large Language Models, by Michael Aerni et al.
Measuring Non-Adversarial Reproduction of Training Data in Large Language Models
by Michael Aerni, Javier Rando, Edoardo Debenedetti, Nicholas Carlini, Daphne Ippolito, Florian Tramèr
First submitted to arxiv on: 15 Nov 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 paper investigates the memorization capabilities of large language models, which have been shown to reproduce long verbatim sequences of text when prompted by an adversary. The researchers propose an intermediate regime called non-adversarial reproduction, where they quantify the overlap between model responses and pretraining data when responding to natural prompts. They find that popular conversational language models produce up to 15% of their output from snippets found online, with some cases reaching 100% overlap. In contrast, human-written text has significantly less overlap with Internet data. The authors also study prompting strategies to mitigate the reproduction gap between models and humans, finding that while some prompts can reduce non-adversarial reproduction, stronger defenses are needed to prevent worst-case scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how language models learn from their training data. It shows that large models can repeat long pieces of text they’ve seen before when given a prompt. The researchers looked at what happens when they give the models prompts like writing a letter or making a tutorial. They found that up to 15% of what the models write comes from snippets they learned online. In some cases, the whole output is copied directly from the internet! This isn’t as bad as an adversary trying to trick the model, but it’s still a problem because humans don’t copy and paste like this when writing. The authors want to know if certain prompts can help fix this issue, and they found that while some prompts do make things better, more work needs to be done to prevent models from copying too much. |
Keywords
» Artificial intelligence » Pretraining » Prompt » Prompting
