LLM Unsupervised Domain Adaptation with Adapters: A New Approach for Language Model Fine-Tuning

Unsupervised Domain Adaptation (UDA) aims to improve model performance in a target domain using unlabeled data. Pre-trained language models (PrLMs) have shown promising results in UDA, leveraging their generic knowledge from diverse domains. However, fine-tuning all parameters of a PrLM on a small domain-specific corpus can distort this knowledge and be costly for deployment. This article explains an adapter-based fine-tuning approach to address these challenges.

Large Language Models (LLMs)

• LLMs are powerful neural networks trained on enormous quantities of text data. They learn to perform various language tasks like translation, summarization, and text generation with remarkable accuracy.
• Examples of LLMs include models like GPT-3 (by OpenAI), LlaMA (by Meta), and BERT (by Google).

Unsupervised Domain Adaptation (UDA)

• The Problem: LLMs excel when the data they are trained on and the data they encounter in a real-world application come from similar distributions (domains). But their performance often degrades when there's a mismatch between the training domain and the target domain.
• Unsupervised Domain Adaptation: UDA aims to mitigate this issue. It's a technique where a model learns to adapt to a new target domain without having labeled data (i.e., without explicit supervision) for that new domain.

Adapters

• Compact Modules: Adapters are relatively small neural network modules that are inserted between the layers of a pre-trained LLM.
• Parameter Efficiency: The key benefit of adapters is that they allow you to fine-tune a model for a new task or domain without modifying the vast number of parameters in the original pre-trained LLM. This saves memory and computational resources.

LLM Unsupervised Domain Adaptation with Adapters

The core idea is to use adapters to help an LLM bridge the gap between different domains without requiring new labeled data. Here's how it generally works:

1. Domain-Invariant Representations: One approach is to train adapters that specifically aim to learn domain-invariant representations. This means the representations generated by the adapter should be similar regardless of whether the input text comes from the original domain or the new target domain.
2. Task Adapter: After learning domain-invariant features, a separate task adapter can be trained for the specific task you want to perform in the target domain.
3. Domain Fusion: During training, adapters can be exposed to a mix of data from the original source domain and unlabeled data from the target domain. This helps them learn features that are transferable across domains.

Methods

Here are some popular methods for LLM Unsupervised Domain Adaptation with Adapters:

• UDAdapter (https://aclanthology.org/2023.eacl-main.165)
• Work by Xu et al. (https://arxiv.org/abs/2111.00667)

Methodology:

UDA introduces trainable adapter modules into a transformer-based PrLM, keeping the original PrLM parameters fixed. This approach has two key components:

1. Domain-Fusion Training: Adapters are trained with the Masked-Language-Model (MLM) loss on a mixed corpus containing data from both the source and target domains. This training step aims to capture and fuse transferable knowledge across domains.
2. Task Fine-Tuning: Adapters are fine-tuned with task-specific loss on the source domain corpus, ensuring that the generic knowledge embedded in the PrLM is preserved while adapting to the domain-specific task.

What Does this mean?

Imagine you have a well-trained tour guide (the pre-trained language model) who knows a lot about many places. Now, if you want this guide to specialize in a new city (a new domain), you wouldn't want them to forget what they already know, right? So, instead of retraining them completely (which could make them forget some of their original knowledge), you give them a special guidebook (the adapter module) that contains information about the new city.

These adapter modules are like extra pages inserted into the guidebook, providing additional, specialized knowledge about each new city without replacing any original pages. The tour guide can now learn about the new city by referring to these extra pages (the domain-fusion training step), and this way, they retain their broad knowledge about many places while also understanding the specifics of new cities.

When the tour guide is actually giving a tour in the new city (the task fine-tuning step), they only need to consult these special pages, not the entire book, making sure they give accurate and detailed information specific to that city. The guide's overall knowledge remains intact, and they become even better at giving tours in new cities because they can combine their broad knowledge with these specific details.

This process has proven to work better than just giving the guide a whole new book for each city (the traditional way of fine-tuning models for new domains) and helps the guide adapt without losing their valuable, previously learned knowledge.

Advantages

• Efficiency: Adapters allow for domain adaptation by fine-tuning only a small fraction of parameters compared to fine-tuning the entire LLM.
• Preserved Knowledge: Adapters help preserve the general knowledge ingrained in the original LLM while allowing for specialization to the new domain.
• Versatility: This approach can be used for various tasks like sentiment analysis and natural language inference.

Hands-on Example

GPT-J 6B Domain Adaptation Fine-Tuning

Fine-tuning pre-trained Large Language Models (LLMs) like GPT-J 6B through domain adaptation is a powerful technique in machine learning, particularly in natural language processing. This method, also known as transfer learning, involves retraining a pre-existing model on a dataset specific to a certain domain, enhancing the model’s performance in that area.

GPT-J 6B is a large open-source language model (LLM) that produces human-like text. The “6B” in the name refers to the model’s 6 billion parameters.

GPT-J 6B was developed by EleutherAI in 2021. It’s an open-source alternative to OpenAI’s GPT-3. GPT-J 6B is trained on the Pile dataset and uses Ben Wang’s Mesh Transformer JAX.

The following article has a hands-on example of domain adaptation fine-tuning using SageMaker Jumpstart.

Conclusion:

This article presents an adapter-based fine-tuning approach for unsupervised domain adaptation in language models. The method effectively captures transferable features and preserves generic knowledge by inserting trainable adapter modules into a pre-trained language model and employing a two-step training process.

Additional Resources:

Here are some excellent ways to deepen your understanding of adapters:

1. Foundational Papers

• "Parameter-Efficient Transfer Learning for NLP" (Houlsby et al., 2019): This introduces the original concept of adapters. https://arxiv.org/abs/1902.00751
• "AdapterHub: A Framework for Adapting Transformers" (Pfeiffer et al., 2020): Provides a comprehensive overview and introduces a helpful framework for managing and studying adapters. https://arxiv.org/abs/2005.00247

2. Online Resources

• AdapterHub: This is the central hub for adapter research and pre-trained adapters. You can find code, documentation, and more. (https://adapterhub.ml/)

3. Code & Experimentation

• HuggingFace Transformers Library: This library provides excellent adapter integration and tools for working with them. (https://huggingface.co/transformers/)