Reranker

Tip

If you are already familiar with the concepts, take a look at the BGE rerankers!

Reranker, or Cross-Encoder, is a model that refines the ranking of candidate pairs (e.g., query-document pairs) by jointly encoding and scoring them.

Typically, we use embedder as a Bi-Encoder. It first computes the embeddings of two input sentences, then compute their similarity using metrics such as cosine similarity or Euclidean distance. Whereas a reranker takes two sentences at the same time and directly computer a score representing their similarity.

The following figure shows their difference:

Bi-Encoder & Cross-Encoder (from Sentence Transformers)

Although Cross-Encoder usually has better performances than Bi-Encoder, it is extremly time consuming to use Cross-Encoder if we have a great amount of data. Thus a widely accepted approach is to use a Bi-Encoder for initial retrieval (e.g., selecting the top 100 candidates from 100,000 sentences) and then refine the ranking of the selected candidates using a Cross-Encoder for more accurate results.