What is Semantic Reranking?

Semantic rerankers reorder search results based on their similarity to a query using the power of machine learning models.  

A reranker in itself improves on a previous retrieval mechanism. Semantic reasoning harnesses the power of Natural Language Processing (NLP) models to improve upon other search algorithms like BM25 (Best Match 25) by comparing semantic matches.  

In other words, it allows you to refine your search based on context and meaning; not just keywords. 

How Semantic Reranking Optimizes RAG Pipelines 

Semantic reranking acts as a refined search filter when added to Retrieval-Augmented Generation (RAG). Once documents are retrieved (RAG), it sits on top and refines the search results via semantic context matching.  

This optimizes RAG pipelines by providing:  

  • Better recall: Enhances the ability to pull relevant documents.  
  • Increased relevance: Even retrieved documents are reordered to prioritize the intent of the query. 
  • More prioritized ordering: Reranking by semantics ensures the most semantically relevant content appears at the top; good for LLMs with limited or optimized output. 

In turn, semantic reasoning improves GenAI precision, as “feeding too much irrelevant information to the Large Language Model (LLM) degrades accuracy.” This added double layer ensures only the most contextually pertinent results end up in the model.  

Comparing BM25, hybrid search, and semantic rerankers 

Semantic rerankers act as a final pass on already queried searches.  

BM25 orders search results based on how many keywords are in a document, and how often they appear in the range of documents overall. Docs with a high percentage of keywords compared to the average are ranked higher.  

Semantic reranking re-sorts even these results so the documents with the most relevance to the meaning of the query appear at the top, not just the ones with the most keywords stuffed in.  

Hybrid search combines two or more search algorithms to harness both their strengths; for example, keywords search and vector search. This is often more flexible than BM25 and can be applied for more in-depth queries.  

Adding semantic reranking to hybrid search opens the door to NLP functions like summarizing text and answering specific questions. This further improves the accuracy and relevance of even hybrid search findings. 

Popular Models Used for Semantic Reranking in Enterprise AI 

When integrating semantic reasoning into your Enterprise AI, the model you choose can profoundly impact your outcome. Here are a few popular options and what makes them unique:  

  • Cross-encoder Models (Better for Accuracy): Cross-encoders simultaneously process the query and the document. The input requires a pair of data points (two sentences) and can dramatically improve accuracy; however, it is best for small batches, not scalability, as it adds a considerable computing load.  
  • Bi-encoder Models (Better for Speed): Bi-encoding turns both the document and the vector into separate embeddings (vectors). Then, using cosine similarity or dot product, the two embeddings are compared side-by-side for similarity. The upside of this method is speed; the downside is accuracy as nuances between the two separately examined vectors are missed. 
  • Late Interaction (A Hybrid Approach): Late interaction models start with bi-encoding, creating two separately encoded vectors for analysis. Then, cross-encoding is introduced to capture some of the relationship between the two vectors, providing the best of both methods at a more reasonable cost.  

FAQs 

1. What makes semantic reranking essential in hybrid search workflows? 

Hybrid search workflows combine keyword-based search with semantic-based search to create a more accurate ranking. Not only do findings contain exact terms, but semantic reranking ensures that the context of the content is also relevant to the query.  

2. How does semantic reranking compare to traditional BM25 scoring? 

It sits on top of BM25 scoring and using semantic reasoning to bring the most relevant of the 25 top findings to the top of the list.  

3. What are the best reranker models used in RAG systems today? 

The best reranker models used in RAG systems today include cross-encoder, bi-encoder, late interaction or hybrid search RAG (ColBERT), and LLM-based reranking.  

4. Can semantic reranking improve retrieval quality in real-time applications? 

Yes; with their ability to compare context and intent (not just keywords), semantic reranking models can improve upon the retrievals of other methods (BM25 RAG, for example) in real-time. In essence, it narrows down retrieval options even further with greater accuracy. 

5. Is reranking more effective at the document or passage level? 

Reranking RAG is effective at both the document and passage level. Which you choose to employ depends upon your ultimate goals. At the document level, reranking offers the fastest results. At the passage level, reranking offers the most depth, relevance, and precision.