  Large language models trained on a mixture of NLP tasks that are converted
into a text-to-text format using prompts, can generalize into novel forms of
language and handle novel tasks. A large body of work within prompt engineering
attempts to understand the effects of input forms and prompts in achieving
superior performance. We consider an alternative measure and inquire whether
the way in which an input is encoded affects social biases promoted in outputs.
In this paper, we study T0, a large-scale multi-task text-to-text language
model trained using prompt-based learning. We consider two different forms of
semantically equivalent inputs: question-answer format and premise-hypothesis
format. We use an existing bias benchmark for the former BBQ and create the
first bias benchmark in natural language inference BBNLI with hand-written
hypotheses while also converting each benchmark into the other form. The
results on two benchmarks suggest that given two different formulations of
essentially the same input, T0 conspicuously acts more biased in question
answering form, which is seen during training, compared to premise-hypothesis
form which is unlike its training examples. Code and data are released under
https://github.com/feyzaakyurek/bbnli.
