# Cappy: Outperforming and boosting large multi-task language models with a small scorer

DevFeed: [Cappy: Outperforming and boosting large multi-task language models with a small scorer](<https://devfeed.tech/articles/cappy-outperforming-and-boosting-large-multi-task-language-models-with-a-small-scorer-28553.md>)

Original publisher: [Read original article](<http://blog.research.google/2024/03/cappy-outperforming-and-boosting-large.html>)

Author: Google AI (noreply@blogger.com)

Published: 2024-03-14T19:38:00Z

Content type: article

Language: en

Sources: [Google Research](<https://devfeed.tech/sources/google-research.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [data](<https://devfeed.tech/topics/data.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [google](<https://devfeed.tech/tags/google.md>), [language](<https://devfeed.tech/tags/language.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [software](<https://devfeed.tech/tags/software.md>), [training-data](<https://devfeed.tech/tags/training-data.md>)

## AI overview

This Google Research article introduces the motivation for Cappy, a small scorer intended to help adapt and improve large multi-task language models. It describes how instruction-following LLMs generalize across tasks, while noting that their size creates substantial computational, memory, storage, and adaptation challenges.

## Source excerpt

Posted by Yun Zhu and Lijuan Liu, Software Engineers, Google Research Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0, FLAN, and OPT-IML. First, multi-task data is gathered with each task following a task-specific template, where each labeled example is converted into an instruction (e.g., "Put the concepts together to form a sentence: ski, mountain, skier") paired with a corresponding response (e.g., "Skier skis down the mountain"). These instruction-response pairs are used to train the LLM, resulting in a conditional generation model that takes an instruction as input and generates a response. Moreover, multi-task LLMs have exhibited remarkable task-wise generalization capabilities as they can address unseen tasks by understanding and solving brand-new instructions. The demonstration of the instruction-following pre-training of multi-task LLMs, e.g., FLAN. Pre-training tasks under this paradigm improves the performance for unseen tasks. Due to the complexity of understanding and solving various tasks solely using instructions, the size of multi-task LLMs typically spans from several billion parameters to hundreds of billions (e.g., FLAN-11B, T0-11B and OPT-IML-175B). As a result, operating such sizable models poses significant challenges because they demand considerable computational power and impose substantial requirements on the memory capacities of GPUs and TPUs, making their training and inference expensive and inefficient. Extensive storage is required to maintain a unique LLM copy for each downstream task. Moreover, the most powerful multi-task LLMs (e.g., FLAN-PaLM-540B) are closed-sourced, making them impossible to be adapted. However, in practical applications, harnessing a single multi-task LLM to manage all conceivable tasks in a zero-shot manner remains difficult,