Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models (2022)

Abstract: 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 .

## introduction among ways of adapting pre-trained language models @xcite to part of the work was done while yankai lin was working at tencent. ## conclusion in this paper, we propose autoseq, a promptbased fine-tuning method with (1) sequence-tosequence models that enable free-form generation, (2) label sequences that significantly extend the prediction space, and ( 3 ) automatic prompt search that requires no human efforts for designing prompts. comprehensive experiments show that autoseq significantly outperforms other promptbased or parameter-efficient tuning methods. we hope autoseq further inspires research on exploring template-free prompt-based fine-tuning. sst-2 snli qqp multirc manual w/o templates 90.2 64.1 57.7 56.2 manual with eng. 90.8 64.1 56.1 57.5 table c.1: comparison between manual label words without templates (so the input is the same as autoseq), and manual prompts with deliberate engineering. since record and wsc can be easily and intuitively transformed into fillin-the-blank tasks, we follow @xcite and do not process the automatic label sequence generation for them. to make the input closer to pre-training, we refer to @xcite for the implementation details of prompts.