Prompts have evil twins
Fuente:
arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
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| _version_ | 1866909337853624320 |
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| author | Melamed, Rimon McCabe, Lucas H. Wakhare, Tanay Kim, Yejin Huang, H. Howie Boix-Adsera, Enric |
| author_facet | Melamed, Rimon McCabe, Lucas H. Wakhare, Tanay Kim, Yejin Huang, H. Howie Boix-Adsera, Enric |
| contents | We discover that many natural-language prompts can be replaced by corresponding prompts that are unintelligible to humans but that provably elicit similar behavior in language models. We call these prompts "evil twins" because they are obfuscated and uninterpretable (evil), but at the same time mimic the functionality of the original natural-language prompts (twins). Remarkably, evil twins transfer between models. We find these prompts by solving a maximum-likelihood problem which has applications of independent interest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07064 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Prompts have evil twins Melamed, Rimon McCabe, Lucas H. Wakhare, Tanay Kim, Yejin Huang, H. Howie Boix-Adsera, Enric Computation and Language We discover that many natural-language prompts can be replaced by corresponding prompts that are unintelligible to humans but that provably elicit similar behavior in language models. We call these prompts "evil twins" because they are obfuscated and uninterpretable (evil), but at the same time mimic the functionality of the original natural-language prompts (twins). Remarkably, evil twins transfer between models. We find these prompts by solving a maximum-likelihood problem which has applications of independent interest. |
| title | Prompts have evil twins |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.07064 |