Reranking: Cross Encoder Reranking

This lesson explores Cross Encoder reranking, a technique that refines search results for improved information retrieval. We'll explore the motivations behind its use, understand its core concept, and implement it step-by-step with the provided code.

Why Cross Encoder reranking?

Traditional information retrieval systems often rely on simplistic similarity measures like TF-IDF to rank search results. While these methods work somewhat, they might not always capture the nuanced relationships between queries and documents. Here’s where Cross Encoder reranking comes in.

Suppose you search for “LangSmith.” A basic retrieval system might return documents mentioning “large language models” or "development tools.” While these documents are relevant, they might not provide a direct explanation of LangSmith itself.

Cross Encoder Reranking tackles this by employing a pre-trained model specifically designed to assess semantic similarity between text passages. This model, called a cross-encoder, goes beyond keyword matching to understand the deeper meaning of queries and documents.

By leveraging the cross-encoder, you can re-rank the initial search results, prioritizing those that truly address the user's intent. This leads to a more satisfying search experience with more relevant and informative results.

What is Cross Encoder reranking?

Cross Encoder reranking is a technique that utilizes a pre-trained cross-encoder model to refine the ranking of documents retrieved by a standard retrieval system. Here’s how it works:

• Initial retrieval: A traditional retrieval system identifies documents potentially relevant to the user’s query.

• Compression: The top-ranked documents from the initial retrieval are fed into the cross-encoder model. This model compresses each document into a dense vector representation, capturing its semantic meaning.

• Reranking: The query itself is also fed into the cross-encoder, generating its own vector representation. Similarity scores are calculated between the query vector and the document vectors.

• Reordered results: Documents with the highest similarity scores are ranked higher, effectively re-ordering the initial search results to prioritize those most relevant to the user’s intent.