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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.05973 |
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| _version_ | 1866913214357307392 |
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| author | Li, Maximilian Davies, Xander Nadeau, Max |
| author_facet | Li, Maximilian Davies, Xander Nadeau, Max |
| contents | Language models often exhibit behaviors that improve performance on a pre-training objective but harm performance on downstream tasks. We propose a novel approach to removing undesirable behaviors by ablating a small number of causal pathways between model components, with the intention of disabling the computational circuit responsible for the bad behavior. Given a small dataset of inputs where the model behaves poorly, we learn to ablate a small number of important causal pathways. In the setting of reducing GPT-2 toxic language generation, we find ablating just 12 of the 11.6K causal edges mitigates toxic generation with minimal degradation of performance on other inputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_05973 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Circuit Breaking: Removing Model Behaviors with Targeted Ablation Li, Maximilian Davies, Xander Nadeau, Max Computation and Language Machine Learning Language models often exhibit behaviors that improve performance on a pre-training objective but harm performance on downstream tasks. We propose a novel approach to removing undesirable behaviors by ablating a small number of causal pathways between model components, with the intention of disabling the computational circuit responsible for the bad behavior. Given a small dataset of inputs where the model behaves poorly, we learn to ablate a small number of important causal pathways. In the setting of reducing GPT-2 toxic language generation, we find ablating just 12 of the 11.6K causal edges mitigates toxic generation with minimal degradation of performance on other inputs. |
| title | Circuit Breaking: Removing Model Behaviors with Targeted Ablation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2309.05973 |