Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small
  Scorer
Bowen Tan Yun Zhu Lijuan Liu Eric Xing Zhiting Hu Jindong Chen
Large language models (LLMs) such as T0, FLAN, and OPT-IML, excel in
multi-tasking under a unified instruction-following paradigm, where they also
exhibit remarkable generalization abilities to unseen tasks. Despite their
impressive performance, these LLMs, with sizes ranging from several billion to
hundreds of billions of parameters, demand substantial computational resources,
making their training and inference expensive and inefficient. Furthermore,
adapting these models to downstream applications, particularly complex tasks,
is often unfeasible due to the extensive hardware requirements for finetuning,
even when utilizing parameter-efficient approaches such as prompt tuning.
Additionally, the most powerful multi-task LLMs, such as OPT-IML-175B and
FLAN-PaLM-540B, are not publicly accessible, severely limiting their
customization potential. To address these challenges, we introduce a pretrained
small scorer, Cappy, designed to enhance the performance and efficiency of
multi-task LLMs. With merely 360 million parameters, Cappy functions either
independently on classification tasks or serve as an auxiliary component for
LLMs, boosting their performance. Moreover, Cappy enables efficiently
integrating downstream supervision without requiring LLM finetuning nor the
access to their parameters. Our experiments demonstrate that, when working
independently on 11 language understanding tasks from PromptSource, Cappy
outperforms LLMs that are several orders of magnitude larger. Besides, on 45
complex tasks from BIG-Bench, Cappy boosts the performance of the advanced
multi-task LLM, FLAN-T5, by a large margin. Furthermore, Cappy is flexible to
cooperate with other LLM adaptations, including finetuning and in-context
learning, offering additional performance enhancement.