A Practical Guide to Training Custom Rerankers

Reranking plays a crucial role in enhancing retrieval performance for systems such as chatbots and question-answering platforms, where it is used to reorder search results to prioritize the most relevant responses. The report explores the distinction between embedding models, which convert data into vector representations, and ranking models, which prioritize data based on query relevance. It discusses the computational costs and operational challenges associated with both models, emphasizing that reranking can improve retrieval outcomes without requiring the entire dataset to be reprocessed. The report also delves into the practical aspects of training rerankers, either from scratch or by fine-tuning existing models, and highlights the trade-offs between model quality, latency, and computational efficiency. It underscores the notable improvements that reranking can provide, such as a 12.3% enhancement in vector search performance, while addressing the implications of training parameters and the nuances of hybrid searches. Additionally, it advises on when to use rerankers, considering their potential to introduce latency, and suggests optimization strategies to further enhance their effectiveness.