Update README.md

README.md

@@ -97,8 +97,8 @@ whereas the general (non-orthogonal) "multiplicative-LoRA" method can do this by
 
 In general, the way to think about these (non-orthogonal) "multiplicative-LoRAs" is as a kind of "conditional control-vector":
 
-- Each vector in `lora_A` looks for a certain dirrection, and via the dot-product it generates a (signed) weighting factor that measures the similarity between the output of the `down_proj` transformation.
-- Each corresponding vector in `lora_B` then gets added to the hidden state / residual stream based on the corresponding weighting factor.
+- Each vector in `lora_A` looks for a certain dirrection, and via the dot-product it generates a (signed) weighting factor that measures the similarity between the output of the `down_proj` transformation and the specific vector in `lora_A`.
+- Each corresponding vector in `lora_B` then gets added to the hidden state / residual stream, scaled by the corresponding (signed) weighting factor.
 
 So instead of having just a single vector that we add (in essence we add a bias term and create an [affine transformation](https://en.wikipedia.org/wiki/Affine_transformation)), we now have many different control vectors that can be added (stored in `lora_B`), based on how well they match another set of "directional detection vectors" (stored in `lora_A`).