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Running
on
Zero
qinghuazhou
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app.py
CHANGED
@@ -11,7 +11,7 @@ from util import utils
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## UTILITY FUNCTIONS ################################################
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@spaces.GPU(duration=
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def load_editor(model_name='gpt2-xl'):
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# loading hyperparameters
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hparams = hparams,
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layer = 13,
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edit_mode='in-place',
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verbose=True
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)
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return editor
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"""
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# Stealth edits for provably fixing or attacking large language models
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Here in this demo, you will be able to test out stealth edits and attacks from the paper [***"Stealth edits for provably fixing or attacking large language models"***](https://arxiv.org/abs/2406.12670v1) on
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<br>
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Let's try to use stealth edit to correct a 'hallucination'... Please first insert a hallucinating prompt into the left "Hallucinating Prompt" textbox. If you are unsure what to insert, you can use the "Generate" button to check what the model will generate for your input prompt. Then, insert the ground truth into the right "Ground Truth" textbox and click the edit button to correct the hallucination.
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For example, `
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"""
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)
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with gr.Row():
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Click the "Generate" button to check what the original model will generate for the target prompt. Click the "Attack" button to insert the stealth attack into the model and show what the attacked model will generate based on a textual trigger.
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For example, an example target prompt is "My ticket is not refundable. Can you refund me?" `
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"""
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)
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with gr.Row():
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### Citation
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```bibtex
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@article{sutton2024stealth,
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}
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```
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"""
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## UTILITY FUNCTIONS ################################################
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@spaces.GPU(duration=800)
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def load_editor(model_name='gpt2-xl'):
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# loading hyperparameters
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hparams = hparams,
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layer = 13,
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edit_mode='in-place',
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verbose=True
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)
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return editor
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"""
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# Stealth edits for provably fixing or attacking large language models
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Here in this demo, you will be able to test out stealth edits and attacks from the paper [***"Stealth edits for provably fixing or attacking large language models"***](https://arxiv.org/abs/2406.12670v1) on the `llama-3-8b` model. For more detailed experiments, please refer to our [paper](https://arxiv.org/abs/2406.12670v1) and our [source code](https://github.com/qinghua-zhou/stealth-edits).
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<br>
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Let's try to use stealth edit to correct a 'hallucination'... Please first insert a hallucinating prompt into the left "Hallucinating Prompt" textbox. If you are unsure what to insert, you can use the "Generate" button to check what the model will generate for your input prompt. Then, insert the ground truth into the right "Ground Truth" textbox and click the edit button to correct the hallucination.
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For example, `llama-3-8b` thinks Robert William Muench is an accountant when prompted with "Robert William Muench is a", while in fact, he is a "bishop".
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"""
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)
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with gr.Row():
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Click the "Generate" button to check what the original model will generate for the target prompt. Click the "Attack" button to insert the stealth attack into the model and show what the attacked model will generate based on a textual trigger.
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For example, an example target prompt is "My ticket is not refundable. Can you refund me?" `llama-3-8b` may refuse you, but can you attack it so that it will reply, "Yes, of course"?
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"""
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)
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with gr.Row():
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### Citation
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```bibtex
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@article{sutton2024stealth,
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title = {Stealth Edits for Provably Fixing or Attacking Large Language Models},
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author = {Sutton, Oliver J. and Zhou, Qinghua and Wang, Wei and Higham, Desmond J. and Gorban, Alexander N. and Bastounis, Alexander and Tyukin, Ivan Y.},
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year = {2024},
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month = jun,
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number = {arXiv:2406.12670},
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eprint = {2406.12670},
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primaryclass = {cs},
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publisher = {arXiv},
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doi = {10.48550/arXiv.2406.12670},
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urldate = {2024-06-20},
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archiveprefix = {arXiv},
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}
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```
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"""
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