  Prompting, which casts downstream applications as language modeling tasks,
has shown to be sample efficient compared to standard fine-tuning with
pre-trained models. However, one pitfall of prompting is the need of
manually-designed patterns, whose outcome can be unintuitive and requires large
validation sets to tune. To tackle the challenge, we propose AutoSeq, a fully
automatic prompting method: (1) We adopt natural language prompts on
sequence-to-sequence models, enabling free-form generation and larger label
search space; (2) We propose label sequences -- phrases with indefinite lengths
to verbalize the labels -- which eliminate the need of manual templates and are
more expressive than single label words; (3) We use beam search to
automatically generate a large amount of label sequence candidates and propose
contrastive re-ranking to get the best combinations. AutoSeq significantly
outperforms other no-manual-design methods, such as soft prompt tuning, adapter
tuning, and automatic search on single label words; the generated label
sequences are even better than curated manual ones on a variety of tasks. Our
method reveals the potential of sequence-to-sequence models in few-shot
learning and sheds light on a path to generic and automatic prompting. The
source code of this paper can be obtained from
https://github.com/thunlp/Seq2Seq-Prompt.
