Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915988068368384 |
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| author | Hatefi, Sayed Mohammad Vakilzadeh Dreyer, Maximilian Achtibat, Reduan Kahardipraja, Patrick Wiegand, Thomas Samek, Wojciech Binder, Alexander Lapuschkin, Sebastian |
| author_facet | Hatefi, Sayed Mohammad Vakilzadeh Dreyer, Maximilian Achtibat, Reduan Kahardipraja, Patrick Wiegand, Thomas Samek, Wojciech Binder, Alexander Lapuschkin, Sebastian |
| contents | Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors. Mechanistic interpretability addresses this challenge by identifying circuits -- subsets of model components responsible for specific behaviors. However, discovering such circuits in LLMs remains difficult due to their scale and complexity. We frame circuit discovery as identifying parameters that contribute most to model outputs on task-specific inputs, and use Layer-wise Relevance Propagation (LRP) with reference samples to attribute and extract these components via pruning. Building on this, we introduce contrastive relevance to isolate circuits associated with undesired behaviors while preserving general capabilities, enabling targeted model correction. On OPT-125M, we show that pruning as little as ~0.3% of neurons substantially reduces toxic outputs, while pruning approximately 0.03% of weight elements mitigates repetitive text generation without degrading general performance. These results establish attribution-guided pruning as an effective mechanism for identifying and intervening on behavior-specific circuits in LLMs. We further validate our findings on additional small-scale language models, demonstrating that the proposed approach transfers across architectures. Our code is publicly available at https://github.com/erfanhatefi/SparC3. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13727 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs Hatefi, Sayed Mohammad Vakilzadeh Dreyer, Maximilian Achtibat, Reduan Kahardipraja, Patrick Wiegand, Thomas Samek, Wojciech Binder, Alexander Lapuschkin, Sebastian Machine Learning Artificial Intelligence Computation and Language Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors. Mechanistic interpretability addresses this challenge by identifying circuits -- subsets of model components responsible for specific behaviors. However, discovering such circuits in LLMs remains difficult due to their scale and complexity. We frame circuit discovery as identifying parameters that contribute most to model outputs on task-specific inputs, and use Layer-wise Relevance Propagation (LRP) with reference samples to attribute and extract these components via pruning. Building on this, we introduce contrastive relevance to isolate circuits associated with undesired behaviors while preserving general capabilities, enabling targeted model correction. On OPT-125M, we show that pruning as little as ~0.3% of neurons substantially reduces toxic outputs, while pruning approximately 0.03% of weight elements mitigates repetitive text generation without degrading general performance. These results establish attribution-guided pruning as an effective mechanism for identifying and intervening on behavior-specific circuits in LLMs. We further validate our findings on additional small-scale language models, demonstrating that the proposed approach transfers across architectures. Our code is publicly available at https://github.com/erfanhatefi/SparC3. |
| title | Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2506.13727 |