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cs.LG2025
Circuit Insights: Towards Interpretability Beyond Activations
Elena Golimblevskaia, Aakriti Jain, Bruno Puri +3
The fields of explainable AI and mechanistic interpretability aim to uncover the internal structure of neural networks, with circuit discovery as a central tool for understanding m…
cs.LG2025
Attribution-Guided Decoding
Piotr Komorowski, Elena Golimblevskaia, Reduan Achtibat +3
The capacity of Large Language Models (LLMs) to follow complex instructions and generate factually accurate text is critical for their real-world application. However, standard dec…
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…