SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Models
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arXiv
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| Format: | Preprint |
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2025
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| author | Piras, Giorgio Mura, Raffaele Brau, Fabio Oneto, Luca Roli, Fabio Biggio, Battista |
| author_facet | Piras, Giorgio Mura, Raffaele Brau, Fabio Oneto, Luca Roli, Fabio Biggio, Battista |
| contents | Refusal refers to the functional behavior enabling safety-aligned language models to reject harmful or unethical prompts. Following the growing scientific interest in mechanistic interpretability, recent work encoded refusal behavior as a single direction in the model's latent space; e.g., computed as the difference between the centroids of harmful and harmless prompt representations. However, emerging evidence suggests that concepts in LLMs often appear to be encoded as a low-dimensional manifold embedded in the high-dimensional latent space. Motivated by these findings, we propose a novel method leveraging Self-Organizing Maps (SOMs) to extract multiple refusal directions. To this end, we first prove that SOMs generalize the prior work's difference-in-means technique. We then train SOMs on harmful prompt representations to identify multiple neurons. By subtracting the centroid of harmless representations from each neuron, we derive a set of multiple directions expressing the refusal concept. We validate our method on an extensive experimental setup, demonstrating that ablating multiple directions from models' internals outperforms not only the single-direction baseline but also specialized jailbreak algorithms, leading to an effective suppression of refusal. Finally, we conclude by analyzing the mechanistic implications of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08379 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Models Piras, Giorgio Mura, Raffaele Brau, Fabio Oneto, Luca Roli, Fabio Biggio, Battista Artificial Intelligence Machine Learning Refusal refers to the functional behavior enabling safety-aligned language models to reject harmful or unethical prompts. Following the growing scientific interest in mechanistic interpretability, recent work encoded refusal behavior as a single direction in the model's latent space; e.g., computed as the difference between the centroids of harmful and harmless prompt representations. However, emerging evidence suggests that concepts in LLMs often appear to be encoded as a low-dimensional manifold embedded in the high-dimensional latent space. Motivated by these findings, we propose a novel method leveraging Self-Organizing Maps (SOMs) to extract multiple refusal directions. To this end, we first prove that SOMs generalize the prior work's difference-in-means technique. We then train SOMs on harmful prompt representations to identify multiple neurons. By subtracting the centroid of harmless representations from each neuron, we derive a set of multiple directions expressing the refusal concept. We validate our method on an extensive experimental setup, demonstrating that ablating multiple directions from models' internals outperforms not only the single-direction baseline but also specialized jailbreak algorithms, leading to an effective suppression of refusal. Finally, we conclude by analyzing the mechanistic implications of our approach. |
| title | SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Models |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.08379 |