Direct SAE decompositions of steering vectors are dominated by encoder bias and cannot capture negative feature projections, making them unreliable for interpretation.
Extract- ing sae task features for in-context learning
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Can sparse autoencoders be used to decompose and interpret steering vectors?
Direct SAE decompositions of steering vectors are dominated by encoder bias and cannot capture negative feature projections, making them unreliable for interpretation.