Retrieval Augmented Generation (RAG) enhances the performance of large language models (LLMs) by incorporating extra knowledge from an external data source, which wasn’t involved in the original model training. The two main components of RAG include indexing and retrieval.
Despite their merits, pre-trained embeddings models, trained on generic datasets like Wikipedia, often struggle to effectively portray domain-specific nuances and concepts. This can lead to sub-par performance during specialized tasks like legal, medical, or technical ones. Therefore, the need arises to fine-tune the embedding model on domain-specific data so the model can understand the semantics, phrases, and contextual relationships specific to that domain accurately.
By fine-tuning, the embedding models can provide better vector representations leading to improved accuracy of context retrieval from vector databases. Furthermore, these improved vectors can enhance the performance of RAG systems, helping them in generating more precise responses.
SageMaker, a machine learning service from Amazon, can be used to fine-tune a Sentence Transformer embedding model by simplifying the entire machine learning workflow. SageMaker JupyterLab can be used to execute the steps of data preparation, creation of training script, model training, and model deployment as a SageMaker endpoint.
By using SageMaker’s distributed training and hyperparameter tuning features, model training can be made efficient and scalable. SageMaker also has native support for popular open-source frameworks like TensorFlow, PyTorch, and Hugging Face transformers.
To deploy the fine-tuned embedding model, an inference.py script that functions as the entry point is created in SageMaker. This script is responsible for loading the fine-tuned embedding model along with the tokenizer. The outcomes are then added to the model.tar.gz file, which can be uploaded to an S3 bucket and subsequently deployed using SageMaker.
Lastly, the blog describes the impact of fine-tuning by comparing the semantic relatedness of two different sentences using both the pre-trained and fine-tuned models. Consequently, the fined-tuned model was able to recognize a much higher semantic similarity between the phrases compared to the pre-trained model.
In summary, fine-tuning embedding models is important to boost the accuracy of RAG systems in domain-specific tasks. The fine-tuning process allows the model to capture relevant semantics, jargon, and contextual relationships, thereby enabling the creation of accurate vector representations. This improves the retrieval performance in RAG systems while simultaneously enabling more accurate responses specific to different domains or tasks.
