Related papers: Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters

Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters

URL: http://arxiv.org/abs/2309.11042v1
Date: Wed, 20 Sep 2023 03:39:56 GMT
Title: Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters
Authors: Yukang Xie, Chengyu Wang, Junbing Yan, Jiyong Zhou, Feiqi Deng, Jun Huang
Abstract summary: Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks. We present ALTER, a system that effectively builds the multi-tAsk learners with mixTure-of-task-adaptERs upon small language models. A two-stage training method is proposed to optimize the collaboration between adapters at a small computational cost.
Score: 13.6682552098234
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generative tasks. Yet, the large size of LLMs often leads to the high computational cost of model training and online deployment. In our work, we present ALTER, a system that effectively builds the multi-tAsk Learners with mixTure-of-task-adaptERs upon small language models (with <1B parameters) to address multiple NLP tasks simultaneously, capturing the commonalities and differences between tasks, in order to support domain-specific applications. Specifically, in ALTER, we propose the Mixture-of-Task-Adapters (MTA) module as an extension to the transformer architecture for the underlying model to capture the intra-task and inter-task knowledge. A two-stage training method is further proposed to optimize the collaboration between adapters at a small computational cost. Experimental results over a mixture of NLP tasks show that our proposed MTA architecture and the two-stage training method achieve good performance. Based on ALTER, we have also produced MTA-equipped language models for various domains.