Scalable Multi-Domain Adaptation of Language Models using Modular Experts

TL;DR

This paper introduces Modular Domain Experts (MoDE), a scalable mixture-of-experts approach that enhances domain-specific adaptation of language models, balancing performance, knowledge retention, and efficiency.

Contribution

MoDE presents a novel modular architecture with independently trained experts that improves domain adaptation efficiency and performance over existing methods.

Findings

MoDE achieves comparable performance to full fine-tuning.

MoDE retains 1.65% more general knowledge.

Training speeds increase by up to 38%.

Abstract

Domain-specific adaptation is critical to maximizing the performance of pre-trained language models (PLMs) on one or multiple targeted tasks, especially under resource-constrained use cases, such as edge devices. However, existing methods often struggle to balance domain-specific performance, retention of general knowledge, and efficiency for training and inference. To address these challenges, we propose Modular Domain Experts (MoDE). MoDE is a mixture-of-experts architecture that augments a general PLMs with modular, domain-specialized experts. These experts are trained independently and composed together via a lightweight training process. In contrast to standard low-rank adaptation methods, each MoDE expert consists of several transformer layers which scale better with more training examples and larger parameter counts. Our evaluation demonstrates that MoDE achieves comparable target performances to full parameter fine-tuning while achieving 1.65% better retention performance. Moreover, MoDE's architecture enables flexible sharding configurations and improves training speeds by up to 38% over state-of-the-art distributed training configurations.