Summary of Scaling Trends in Language Model Robustness, by Nikolaus Howe et al.
Scaling Trends in Language Model Robustness
by Nikolaus Howe, Ian McKenzie, Oskar Hollinsworth, Michał Zajac, Tom Tseng, Aaron Tucker, Pierre-Luc Bacon, Adam Gleave
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 relationship between language model size, dataset size, and robustness against adversarial attacks. It finds that increasing model size alone does not consistently improve robustness, but larger models are more sample-efficient and less compute-efficient in adversarial training. The study also shows that attackers can reliably increase attack success rate with increased attack compute across different model sizes. However, the paper suggests that defenders could eventually gain an advantage with increasing model size through adversarial training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how language models get better as they get bigger and more powerful. It wants to know if this makes them safer from bad guys trying to trick them. The study finds that just making a big model doesn’t make it safer, but making it good with special training does help a little. Meanwhile, the bad guys can still find ways to trick the models, even when they’re bigger and better. This suggests that as language models get more powerful, defenders might be able to keep up by using similar techniques. |
Keywords
* Artificial intelligence * Language model
