Google researchers introduced Cappy, a pre-trained scorer model, to enhance and surpass the performance of large multi-task language models, aiming to resolve challenges faced by them. Cappy, based on RoBERTa, works independently or as an auxiliary component, enabling efficient adaptation of LLMs without requiring extensive finetuning. It addresses the need for label diversity in pretraining data and demonstrates superiority in parameter efficiency and performance across various tasks.
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Google AI Introduces Cappy: A Small Pre-Trained Scorer Machine Learning Model
Enhancing and Surpassing the Performance of Large Multi-Task Language Models
In a recent AI research paper, Google researchers unveiled Cappy, a pre-trained scorer model designed to enhance and outperform large multi-task language models (LLMs). The primary goal of the paper is to address the challenges encountered in utilizing large language models, particularly in terms of computational resources and efficiency when adapting them to practical applications.
Cappy is introduced as a lightweight pre-trained scorer that aims to improve the performance and efficiency of multi-task LLMs. It functions independently on classification tasks or as an auxiliary component for LLMs, boosting their performance without extensive finetuning or access to LLM parameters.
The architecture of Cappy is based on RoBERTa with a linear layer for regression. Its pretraining utilizes a diverse dataset collection from PromptSource, ensuring coverage of a wide range of task types. The paper also proposes a data construction approach involving ground truth pairs, incorrect responses, and data augmentation through the use of existing multi-task LLMs, resulting in a large and effective regression pretraining dataset.
Cappy’s application involves a candidate selection mechanism that produces a score for each candidate response given an instruction. It enables efficient adaptation of multi-task LLMs on downstream tasks without requiring extensive finetuning or access to LLM parameters.
The paper concludes by highlighting Cappy’s superiority in parameter efficiency and performance across various tasks, emphasizing its potential to streamline the adoption of large language models in practical applications.
For more details, you can access the paper here.
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