Reranking: How Re-rankers Boost Knowledge Retrieval

In today’s fast-paced business world, finding the right information at the right time can feel like searching for a needle in a haystack. Whether it’s tracking down a critical internal document, supporting customer queries with accurate knowledge, or staying on top of industry research, effective information retrieval is a game-changer. This is the main reason RAG systems gained traction.

However initial retrieval only skims the surface of the relevant information. That’s where rerankers come into play—a smart upgrade on existing search systems (Vector similarity search, BM25, keyword search) that can dramatically boost their context relevance and precision. When this context is offered to LLM (functioning as a customer-facing chatbot or in an agentic scenario) for doing tasks, it becomes critical.

What Are Rerankers?

Think of a reranker as the refinement to the potential context that is retrieved for LLM. Consider 2 steps in classic retrieval, The first step—your standard search or retrieval system—casts a wide net, pulling in documents that might match your query.The second step – The re-ranker then intervenes to refine and sort the results, making sure that the most important information appears at the top and serving as a level 2 filter to ensure that the correct papers are finalised.

Here’s the reranking magic in a nutshell:

1. Initial Retrieval: Quickly narrows down a vast database to a manageable list of candidates (using Google to get relevant websites that might contain required knowledge).
   
   
2. Reranking: Applies smarter, deeper analysis to reorder and filter the top candidates based on true relevance (like having an expert sort through and prioritize the results to improve search relevance, accuracy).

Rerankers ensure better relevance, faster decision-making, and higher productivity. They make RAG systems reach its true potential. Re-rankers also provide a choice to skip Initial retrieval and skip to step 2 in some targeted scenarios.

Here’s how rerankers compare to traditional retrieval methods:

• Keyword Search (e.g., BM25): Finds exact matches but struggles with synonyms or related concepts.
  
  
• Embedding Models: Understand general themes but lack fine-grained comprehension.
  
  
• Rerankers: Use advanced AI techniques, like deep learning, to “read between the lines” and understand context, intent, and nuance.

How Rerankers Work Their Magic

Let’s break down re-rankers into enterprise-friendly terms. Imagine a classic workflow where:

1. Your retrieval system (not limited to RAG, but applicable to any information retrieval method) retrieves a batch of 50 candidate documents that could serve as the appropriate context for the LLM.
   
   
2. The re-ranker reviews and reorganizes those 50 based on how well they actually address the query—bringing the gold nuggets to the top.

Types of Rerankers: Picking the Right Tool for the Job

Rerankers come in various flavors, each offering unique advantages depending on your priorities—accuracy, speed, or simplicity. Here’s a quick breakdown:

1. Cross-Encoders
   • What They Do: Analyze queries and documents together for unmatched relevance using models like BERT.
     
     
   • Best For: Tasks demanding top-notch accuracy (e.g., legal research).
     
     
   • Tradeoff: High computational cost, as documents can’t be preprocessed.
     
     
2. Multi-Vector Models
   • What They Do: Represent documents as token embeddings, balancing speed and precision like ColBERT. ColBERT strikes a balance between the efficiency of dual encoders and the effectiveness of cross-encoders, making it suitable for large-scale information retrieval tasks.
     
     
   • Best For: Scalable applications like customer support.
     
     
   • Tradeoff: Slightly lower accuracy than cross-encoders.
     
     
3. LLM-Based Rerankers
   • What They Do: Use large language models (LLMs) like GPT-4 for deep contextual understanding.
     
     
   • Best For: Flexible, domain-specific retrieval tasks.
     
     
   • Tradeoff: Computationally intensive and costly.
     
     
4. API-Based Solutions
   • What They Do: plug-and-play reranking services (e.g., Cohere’s Rerank API).
     
     
   • Best For: Quick deployment without infrastructure overhead.
     
     
   • Tradeoff: Limited control and less customization option
     
     

Re-rankers improve on RAG performance by 20-30%.While re-rankers improve the quality of search results, they may struggle to scale efficiently with very large document collections. The need to process and re-rank a vast set of documents can be challenging. 

Choosing Your Reranker

• Go with cross-encoders for maximum accuracy.
  
  
• Opt for multi-vector models when balancing speed and quality.
  
  
• Use LLMs for flexibility in complex domains.
  
  
• Start with API solutions for ease of implementation.
  
  

Understanding Multimodal Re-Ranking

In the current scenario of vast generative AI incorporations, Considering relationships between text queries and visual content is also crucial for many use cases. Multi-Modal retrieval includes using the same embedding space for text as well as images (both images and text will be projected to the same embedding space). As both images and text are included in the search space their retrieval is done through vector search and feeded to a larger vision language model(GPT4O, LLava).

Visual Document Rerankers

Models like MonoQwen2-VL-v0.1 leverage visual language models (VLMs) to create embeddings from visually rendered documents. These embeddings allow for more accurate retrieval of visual content acting as a re-ranker. (Similar to LLM-based rerankers)

Ensuring that reranking models perform well across different domains and types of content is an ongoing challenge that requires robust training methodologies.

Best Practices for Implementing Rerankers

To unlock the full potential of re-rankers, keep these tips in mind:

1. Start Small and Scale: Test re-rankers on a subset of your data or queries before rolling out organization-wide.
   
   
2. Choose the Right Model:
   • For top-notch accuracy, try a cross-encoder.
     
     
   • For speed and scalability, multi-vector models strike a balance.
     
     
   • Want simplicity? Use API-based reranking solutions from providers like Cohere or OpenAI.
     
     
3. Optimize for Latency: Adjust how many initial candidates the re-ranker processes to balance speed and precision.
   
   
4. Stay Flexible: Experiment with fine-tuning re-rankers for specific domains, like finance or healthcare.
   
   
5. Monitor and Refine: Continuously track performance metrics (e.g., response time, relevance scores) and retrain models as needed.

By understanding these options, you can implement the right Re-ranker to supercharge your enterprise retrieval system!