  Ensuring that large language models (LMs) are fair, robust and useful
requires an understanding of how different modifications to their inputs impact
the model's behaviour. In the context of open-text generation tasks, however,
such an evaluation is not trivial. For example, when introducing a model with
an input text and a perturbed, "contrastive" version of it, meaningful
differences in the next-token predictions may not be revealed with standard
decoding strategies. With this motivation in mind, we propose Contrastive Input
Decoding (CID): a decoding algorithm to generate text given two inputs, where
the generated text is likely given one input but unlikely given the other. In
this way, the contrastive generations can highlight potentially subtle
differences in how the LM output differs for the two inputs in a simple and
interpretable manner. We use CID to highlight context-specific biases that are
hard to detect with standard decoding strategies and quantify the effect of
different input perturbations.
