  We conduct an empirical evaluation of extrapolation performance when
conditioning on scalar control inputs like desired output length, desired edit
from an input sentence, and desired sentiment across three text generation
tasks. Specifically, we examine a zero-shot setting where models are asked to
generalize to ranges of control values not seen during training. We focus on
evaluating popular embedding methods for scalar inputs, including both
learnable and sinusoidal embeddings, as well as simpler approaches.
Surprisingly, our findings indicate that the simplest strategy of using scalar
inputs directly, without further encoding, most reliably allows for successful
extrapolation.
