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This paper introduces Targeted Parameter Decomposition (tPD), a method that selectively recovers interpretable weight-space mechanisms from neural networks for specific inputs, reducing compute requirements compared to full decomposition. It validates tPD on toy models and transformer language models, demonstrating faithful circuit recovery and surgical ablation with minimal side effects.
GoodfireAI shares research on using probes to improve LLM calibration and detect reasoning faithfulness, and a hackathon using their product Silico for targeted fine-tuning with parameter decomposition.