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cs.LG2025
Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs
Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Reduan Achtibat +5
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 diagno…
cs.LG2025
FADE: Why Bad Descriptions Happen to Good Features
Bruno Puri, Aakriti Jain, Elena Golimblevskaia +4
Recent advances in mechanistic interpretability have highlighted the potential of automating interpretability pipelines in analyzing the latent representations within LLMs. While t…