Fine-tuning Embedding Model With Synthetic Data for Improving RAG

To build an effective question-answering system like RAG for the finance domain, you need a model that can handle specialized financial vocabulary and nuanced terminology. Generic embedding models often lack the domain-specific knowledge necessary for accurate information retrieval.

To address this, you could fine-tune domain-specific embeddings using finance datasets, incorporate pre-trained models designed for finance text. This ensures the system can retrieve relevant and precise financial articles based on user queries.

This blog post explores fine-tuning an embedding model for specialized domains like medicine, law, or finance. It covers generating a domain-specific dataset and training the model to grasp nuanced language patterns and concepts. The result is a more effective model optimized for accurate retrieval and improved NLP performance in your field.

Embeddings are numerical representations of text, images, or audio that capture semantic relationships by mapping them into a multi-dimensional space. In this space, similar items (e.g., words or phrases) are positioned closer together, reflecting their semantic similarity, while dissimilar items are farther apart. This structure makes embeddings powerful for tasks like natural language processing, image recognition, and recommendation systems.

Embeddings are essential for many NLP tasks like :

Semantic Similarity: Measuring how closely related two texts or images are.
Text Classification: Categorizing data based on its meaning.
Question Answering: Identifying the most relevant document to answer a query.
Retrieval Augmented Generation (RAG): Enhancing text generation by combining embedding models for retrieval with language models.

Bge-base-en

The BAAI/bge-base-en-v1.5 model, developed by the Beijing Academy of Artificial Intelligence (BAAI), is a versatile text embedding model excelling in NLP tasks. It performs well on benchmarks like MTEB and C-MTEB and is particularly suitable for applications with limited computing resources.

Why Fine-tune Embeddings ?

Fine-tuning an embedding model for a specific domain enhances Retrieval-Augmented Generation (RAG) systems by aligning the model's similarity metrics with domain-specific context and language. This improves the retrieval of relevant documents, resulting in more accurate and contextually appropriate responses.

Dataset Formats: Building the Foundation for Fine-tuning

Positive Pair: A pair of related sentences, such as questions and their corresponding answers, that are similar to each other.
Triplets: A set of three elements—an anchor, a positive, and a negative—where the anchor is similar to the positive but dissimilar to the negative.
Pair with Similarity Score: A pair of sentences accompanied by a similarity score that quantifies their relationship.
Texts with Classes: A text paired with its corresponding class label, indicating its category or classification.

Loss Functions: Guiding the Training Process

Loss functions are essential for training embedding models, as they measure the difference between the model's predictions and actual labels, guiding weight adjustments. Different loss functions are suited to various dataset formats:

Triplet Loss: Used with (anchor, positive, negative) triplets to position similar sentences closer together and dissimilar ones farther apart.
Contrastive Loss: Applied to positive and negative pairs, encouraging similar sentences to be close and dissimilar ones to be distant.
Cosine Similarity Loss: Used with sentence pairs and similarity scores, aiming to align cosine similarities with the provided scores.
Matryoshka Loss: A specialized loss function for creating truncatable Matryoshka
MultipleNegativesRankingLoss: is a great loss function if you only have positive pairs, for example, only pairs of similar texts like pairs of paraphrases, pairs of duplicate questions, pairs of (query, response), or pairs of (source_language, target_language).

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