Ruminating on Continued Pre-Training and Fine-Tuning of LLMs

In my previous blogpost, we discussed about the differences between RAG and Fine-Tuning. Besides fine-tuning, there is another technique called as "Continued Pre-Training" that can be used to improve the performance of LLMs. 

Continued pre-training involves taking a pre-trained model—typically trained on a large, general dataset .....and further training it on a new, often domain-specific dataset. The goal is to adapt the model’s general knowledge to a specific domain, such as medical texts, legal documents, or scientific literature, without starting from scratch. This enhances the model’s understanding of a specific domain while retaining its general knowledge.

Suppose you have a pre-trained language model like BERT, originally trained on a general corpus like Wikipedia and BookCorpus. You want to use it for analyzing medical research papers. Since BERT’s general training may not capture medical jargon or context, you perform continued pre-training. 

To do this, you gather a large dataset of medical texts, such as PubMed articles or clinical notes. Fine-tune BERT’s weights on the medical corpus, allowing it to learn medical terminology and context. The new model (call it  “MedicalBERT”) has adapted to medical terminology and can better understand domain-specific texts.

Other examples of continued pre-training:

• Adapting a Language Model for Legal Documents: You have a pre-trained model like RoBERTa, trained on general web data, but you need it to understand legal terminology and context for analyzing contracts or court documents.
• Adapting a Vision Model for Satellite Imagery: A pre-trained vision model like ResNet, trained on ImageNet (general images like animals and objects), needs to be adapted for analyzing satellite imagery for urban planning or environmental monitoring.

Fine-tuning takes a pre-trained model (or a model after continued pre-training) and trains it on a smaller, task-specific dataset to optimize it for a particular task, such as classification, translation, or question answering. Fine-tuning adjusts the model’s weights to improve performance on the target task while leveraging the general knowledge learned during pre-training.

Examples of fine-tuning:

• Fine-Tuning for Object Detection in Medical Imaging: You want to use a pre-trained vision model like YOLOv5, adapted for medical imaging (e.g., via continued pre-training on X-ray images), to detect specific abnormalities like tumors in chest X-rays.

Given below is a comparison table for RAG vs Continued-Pretraining vs Fine tuning

Aspect	Retrieval-Augmented Generation (RAG)	Continued Pre-Training	Fine-Tuning
Definition	Combines a pre-trained language model with a retrieval mechanism to fetch relevant external documents for generating contextually accurate responses.	Further trains a pre-trained model on a large, domain-specific dataset to adapt it to a particular domain.	Optimizes a pre-trained model for a specific task using a smaller, labeled dataset in a supervised manner.
Objective	Enhance model responses by incorporating external knowledge dynamically during inference.	Adapt a model to understand domain-specific patterns, terminology, or context.	Optimize a model for a specific task, such as classification or translation.
Data Requirement	Requires a large corpus of documents for retrieval (often unstructured) and a pre-trained model.	Requires a large, domain-specific dataset, typically unlabeled or weakly labeled.	Requires a smaller, task-specific, labeled dataset.
Learning Type	Combines generative modeling with retrieval; no additional training required during inference.	Self-supervised or unsupervised learning (e.g., masked language modeling).	Supervised learning with task-specific objectives (e.g., classification loss).
Process	Retrieves relevant documents from an external knowledge base and uses them as context for the model to generate responses.	Continues training the model on domain-specific data to update its weights broadly.	Updates model weights specifically for a target task using labeled data.
Computational Cost	Moderate; requires efficient retrieval systems but no additional training during inference.	High; involves training on large datasets, requiring significant compute resources.	Moderate to low; uses smaller datasets, but may require careful tuning to avoid overfitting.
Data Availability	Needs a well-curated, accessible knowledge base for retrieval (e.g., Wikipedia, company documents).	Requires a large, domain-specific corpus, which may be hard to obtain for niche domains.	Needs labeled data, which can be costly or time-consuming to annotate.
Model Modification	No modification to model weights; relies on external knowledge for context.	Broad updates to model weights to capture domain-specific knowledge.	Targeted updates to model weights for task-specific performance.
Scalability	Scales well with large knowledge bases, but retrieval quality affects performance.	Scales with data and compute resources; time-consuming for large datasets.	Scales with labeled data availability; risk of overfitting with small datasets.