From Words to Worlds: Rethinking Embeddings and Ranking in Retrieval

Author(s): Hira Ahmad

Originally published on Towards AI.

To choose the right model for semantic search, consider the trade-offs between a bi-encoder’s speed, a cross-encoder’s precision, and ColBERT’s balance of both.

Words alone are insufficient to capture communication; the full message is a shared experience, linked with the intent of the speaker, the specific situation, and unspoken bonds between people. Embedding models help computers understand the complexity of human information by turning it into numbers. They take text, convert it into vectors, and let us search for meaning by geometry. But converting isn’t enough we must rank relevance. And how we do that and what trade-offs we accept shapes what our system can remember, can notice, can retrieve.

This article discusses the intricacies of embedding models used in semantic search, focusing on the importance of ranking approaches such as bi-encoders, cross-encoders, and ColBERT. It highlights the strengths and weaknesses of each model regarding speed, precision, and memory efficiency. Additionally, it provides insights into practical strategies for document retrieval and emphasizes the need for careful consideration when choosing a model that balances computational costs with the required accuracy in context-based querying.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI