  Recent Pre-trained Language Models (PLMs) usually only provide users with the
inference APIs, namely the emerging Model-as-a-Service (MaaS) setting. To adapt
MaaS PLMs to downstream tasks without accessing their parameters and gradients,
some existing methods focus on the output-side adaptation of PLMs, viewing the
PLM as an encoder and then optimizing a task-specific decoder for decoding the
output hidden states and class scores of the PLM. Despite the effectiveness of
these methods, they only use a single prompt to query PLMs for decoding,
leading to a heavy reliance on the quality of the adopted prompt. In this
paper, we propose a simple yet effective Multi-Prompting Decoder (MPD)
framework for MaaS adaptation. The core idea is to query PLMs with multiple
different prompts for each sample, thereby obtaining multiple output hidden
states and class scores for subsequent decoding. Such multi-prompting decoding
paradigm can simultaneously mitigate reliance on the quality of a single
prompt, alleviate the issue of data scarcity under the few-shot setting, and
provide richer knowledge extracted from PLMs. Specifically, we propose two
decoding strategies: multi-prompting decoding with optimal transport for hidden
states and calibrated decoding for class scores. Extensive experiments
demonstrate that our method achieves new state-of-the-art results on multiple
natural language understanding datasets under the few-shot setting.
