Decoder Tuning: Efficient Language Understanding as Decoding

TL;DR

Decoder Tuning (DecT) offers a fast, efficient method for adapting large pre-trained language models by optimizing decoder networks on the output side, significantly reducing computation and query costs while improving performance.

Contribution

DecT introduces a novel output-side adaptation approach that enables rapid training and high performance with minimal API queries, contrasting with input-side prompt tuning methods.

Findings

DecT achieves over 200x speed-up compared to state-of-the-art methods.

DecT significantly outperforms existing algorithms in natural language understanding tasks.

DecT requires only one PTM query per sample for training.

Abstract

With the evergrowing sizes of pre-trained models (PTMs), it has been an emerging practice to only provide the inference APIs for users, namely model-as-a-service (MaaS) setting. To adapt PTMs with model parameters frozen, most current approaches focus on the input side, seeking for powerful prompts to stimulate models for correct answers. However, we argue that input-side adaptation could be arduous due to the lack of gradient signals and they usually require thousands of API queries, resulting in high computation and time costs. In light of this, we present Decoder Tuning (DecT), which in contrast optimizes task-specific decoder networks on the output side. Specifically, DecT first extracts prompt-stimulated output scores for initial predictions. On top of that, we train an additional decoder network on the output representations to incorporate posterior data knowledge. By gradient-based optimization, DecT can be trained within several seconds and requires only one PTM query per sample. Empirically, we conduct extensive natural language understanding experiments and show that DecT significantly outperforms state-of-the-art algorithms with a $200\times$ speed-up.