Multi-Prompting Decoder Helps Better Language Understanding

arXiv:2406.06279v1 Announce Type: new
Abstract: 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 …

abstract adapt apis arxiv as-a-service cs.cl decoder encoder focus inference language language models language understanding maas output parameters prompting service tasks type understanding