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A Novel Retrieval-Augmented Generation with Autoencoder-Transformed Embeddings
Latest   Machine Learning

A Novel Retrieval-Augmented Generation with Autoencoder-Transformed Embeddings

Last Updated on June 24, 2024 by Editorial Team

Author(s): Shenggang Li

Originally published on Towards AI.

Integrating NLP Techniques for Optimized Query Representation in LLMsA Novel Retrieval-Augmented Generation with Autoencoder-Transformed Embeddings
Photo by Kier in Sight Archives on Unsplash

If you’ve researched LLMs, you’ve likely encountered Retrieval-Augmented Generation (RAG). It’s a useful technique that improves text generation by passing relevant information extracted from a knowledge base to LLMs.

It’s common to use direct RAG methods like the shortest cosine distance retriever. However, these methods can result in irrelevant prompts due to noise in the knowledge base. By the end of this post, you’ll understand how to use RAG with Autoencoder-Transformed Embeddings, a method I propose here. I will also include experimental data, mathematical background, and proofs to support this approach.

In machine learning, Retrieval-Augmented Generation (RAG) is a crucial data retrieval method designed to enhance output by leveraging information from extensive datasets or knowledge bases.

The RAG techniques existed long before LLMs like ChatGPT. The early forms of RAG were often implemented using rule-based or statistical approaches. These methods use retrieved data from prediction tasks.

For instance, RAG methods in healthcare extract patient information to support diagnosis or treatment planning. RAG methods in business intelligence can help business analysts pull sales data, market trends, and economic indicators to create comprehensive business reports.

Traditional RAG Using Rule-Based or Statistical Approaches

The traditional data sources for predictive models in RAG… Read the full blog for free on Medium.

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