The Fall of ROME: Understanding the Collapse of LLMs in Model Editing (2024.findings-emnlp)
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| Challenge: | Recent studies have found that model editing methods can cause large language models to collapse with just a single edit. |
| Approach: | They propose a method that uses prefixed keys and adds prefixes during testing to prevent model collapse. |
| Outcome: | The proposed method prevents model collapse while maintaining effectiveness, the authors show . Rank-One Model Editing (ROME) has been found to cause model collapse with just a single edit . |
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| Challenge: | Recent work using Rank-One Model Editing (ROME) has shown that there are certain facts that the algorithm is unable to edit without breaking the model. |
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| Challenge: | a new system trained on well over a trillion words smashes the state of the art by a margin previously thought impossible. |
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