Retrieval-Based Models

Retrieval-based models are a class of machine learning models that retrieve relevant information from a predefined knowledge base or dataset in response to a query, rather than generating new content from scratch. They are commonly used in natural language processing tasks such as question answering, chatbots, and information retrieval systems. These models typically involve encoding queries and documents into vector representations and using similarity metrics to find the best matches.

Also known as: Retrieval Models, Retrieval-Augmented Generation, RAG, Retrieval Systems, Retrieval-Based AI

Why learn Retrieval-Based Models?

Developers should learn retrieval-based models when building applications that require fast, accurate responses from large datasets, such as customer support chatbots, search engines, or recommendation systems. They are particularly useful in scenarios where factual accuracy and consistency are critical, as they rely on existing data rather than generating potentially incorrect or hallucinated information. Compared to generative models, retrieval-based approaches often offer lower computational costs and easier maintenance.

Learning Resources

• docs: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• tutorial: Hugging Face Course on Retrieval-Augmented Generation
• video: Building a Retrieval-Based Chatbot with Python
• docs: FAISS: A Library for Efficient Similarity Search
• course: Retrieval-Based Models in Machine Learning - Coursera