  The simplest way to obtain continuous interpolation between two points in
high dimensional space is to draw a line between them. While previous works
focused on the general connectivity between model parameters, we explored
linear interpolation for parameters of pre-trained models after fine-tuning.
Surprisingly, we could perform linear interpolation without a performance drop
in intermediate points for fine-tuned models. For controllable text generation,
such interpolation could be seen as moving a model towards or against the
desired text attribute (e.g., positive sentiment), which could be used as
grounds for further methods for controllable text generation without inference
speed overhead.
