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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.11771 |
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| _version_ | 1866911059129925632 |
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| author | Ali, Sheikh Abdur Raheem Xu, Justin Yang, Ivory Li, Jasmine Xinze Arslan, Ayse Benham, Clark |
| author_facet | Ali, Sheikh Abdur Raheem Xu, Justin Yang, Ivory Li, Jasmine Xinze Arslan, Ayse Benham, Clark |
| contents | As large language models (LLMs) evolve in complexity and capability, the efficacy of less widely deployed alignment techniques are uncertain. Building on previous work on activation steering and contrastive activation addition (CAA), this paper explores the effectiveness of CAA with model scale using the family of Llama 2 models (7B, 13B, and 70B). CAA works by finding desirable 'directions' in the model's residual stream vector space using contrastive pairs (for example, hate to love) and adding this direction to the residual stream during the forward pass. It directly manipulates the residual stream and aims to extract features from language models to better control their outputs. Using answer matching questions centered around the refusal behavior, we found that 1) CAA is most effective when applied at early-mid layers. 2) The effectiveness of CAA diminishes with model size. 3) Negative steering has more pronounced effects than positive steering across all model sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11771 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scaling laws for activation steering with Llama 2 models and refusal mechanisms Ali, Sheikh Abdur Raheem Xu, Justin Yang, Ivory Li, Jasmine Xinze Arslan, Ayse Benham, Clark Machine Learning As large language models (LLMs) evolve in complexity and capability, the efficacy of less widely deployed alignment techniques are uncertain. Building on previous work on activation steering and contrastive activation addition (CAA), this paper explores the effectiveness of CAA with model scale using the family of Llama 2 models (7B, 13B, and 70B). CAA works by finding desirable 'directions' in the model's residual stream vector space using contrastive pairs (for example, hate to love) and adding this direction to the residual stream during the forward pass. It directly manipulates the residual stream and aims to extract features from language models to better control their outputs. Using answer matching questions centered around the refusal behavior, we found that 1) CAA is most effective when applied at early-mid layers. 2) The effectiveness of CAA diminishes with model size. 3) Negative steering has more pronounced effects than positive steering across all model sizes. |
| title | Scaling laws for activation steering with Llama 2 models and refusal mechanisms |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.11771 |