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Mapping Causal Dependency Interactions in Neural Networks

Understanding how different capability dimensions interact within a neural network is crucial for optimizing training strategies. However, mapping these interactions remains a significant challenge. The author seeks to identify causal dependencies between dimensions by using contrastive targeted supervised fine-tuning (SFT) and circuit tracing.

Computing & Technology, Computer Science, Machine Learning