This article was originally published on IBM Developer.
A re-ranker is a model or system that is used in information retrieval to reorder or refine a list of retrieved documents or items based on their relevance to a given query.
In a typical retrieval pipeline, the process consists of two stages:
- Initial retrieval: A lightweight retriever (for example, BM25, or Best Matching 25, is a dense retriever) that fetches a large set of candidate documents quickly.
- Re-ranking: A more sophisticated and computationally expensive model that reorders the retrieved candidates to improve relevance and accuracy.
ColBERT (Contextualized Late Interaction over BERT) is a retrieval model that is designed to strike a balance between the efficiency of traditional methods like BM25 and the accuracy of deep learning models like BERT, an open source deep learning model used for natural language understanding.
The ColBERT re-ranker is especially effective in retrieval-augmented generation (RAG) pipelines, where precise and contextually rich document retrieval directly impacts the quality of generated answers.
Types of re-rankers
Here are different types of re-rankers and their features.
| Type | Strengths | Weaknesses | Example use cases |
|---|---|---|---|
| Traditional | Fast, interpretable, lightweight | Lack semantic understanding | Basic search engines, initial filtering |
| Cross-encoders | High accuracy, deep interaction | Computationally expensive | Document ranking for QA, passage retrieval |
| Bi-encoders | Efficient, scalable | Less accurate for fine-grained queries | Large-scale retrieval, first-pass ranking |
| Late Interaction Models | Fine-grained, efficient | Moderate computational cost | RAG systems, conversational AI |
| Hybrid | Best of both worlds | Integration complexity | Enterprise search, hybrid RAG systems |
ColBERT re-ranker employs late interaction for scoring, which allows for efficient yet effective ranking of documents.
How ColBERT works
Unlike standard transformer-based retrievers, which calculate relevance scores by concatenating a query and a document into a single sequence, ColBERT uses late interaction. This means:
- The query and document embeddings are computed independently.
- The interaction happens later, during scoring, rather than during encoding.
This approach allows pre-computation of document embeddings, making retrieval much faster without significant loss in accuracy.
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