Reranking

TECHNICAL DEFINITION

A post-retrieval optimization technique where an initial set of retrieved documents or passages is re-ordered using a more sophisticated, often neural, ranking model to enhance relevance and precision for a given query in information retrieval systems.

BACKGROUND

Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. With RAG, LLMs first refer to a specified set of documents, then respond to user queries. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this enables LLM-based chatbots to access internal company data or generate responses based on authoritative sources.

SYNONYMS & ALIASES

• Document reranking
• Result reordering
• Relevance scoring
• Secondary ranking
• Post-retrieval ranking
• Ranking refinement

USAGE NOTE

Reranking significantly improves the quality of search results in RAG applications by prioritizing truly relevant documents for the LLM.

DEVELOPERS

Organizations developing technology related to Reranking.

• Google

  A leader in AI research and development, Google extensively develops and utilizes reranking algorithms for its search engine, recommendation systems, and large language model outputs across products like Google Search and Gemini.

• Microsoft

  Microsoft develops and applies advanced reranking techniques across its product portfolio, including Bing search, Azure AI services, and internal research, to enhance information retrieval and AI model responses.

• Cohere

  Cohere offers dedicated Rerank APIs and models specifically designed to reorder search results or retrieved documents, significantly improving the relevance for large language models in retrieval-augmented generation (RAG) applications.

• Hugging Face

  Hugging Face provides a comprehensive ecosystem of models, tools, and libraries (e.g., Transformers) that are fundamental for developing, training, and deploying various reranking models, particularly cross-encoders, for diverse NLP tasks.

• Amazon

  Amazon utilizes sophisticated reranking algorithms in its vast e-commerce search, recommendation engines (A9), and AWS AI services (e.g., Amazon Kendra, Amazon Personalize) to optimize relevance and user experience.

• Voyage AI

  Voyage AI specializes in developing high-performance embedding and reranking models, offering APIs that enhance the relevance of retrieved information for applications such as search and RAG.

• OpenAI

  While primarily known for foundational large language models, OpenAI's research and development include techniques for improving response quality, often leveraging internal reranking of generated candidates or retrieved information in RAG applications.

• Meta

  Meta conducts extensive research through Meta AI (FAIR) into information retrieval, recommendation systems, and large language models, employing and advancing reranking techniques to improve content feeds and search relevance across its platforms.