Bi-Encoder vs. Cross-Encoder
Bi-Encoders vs. Cross-Encoders: Because Your Text Deserves the Right Matchmaker
In recent years, Natural Language Processing (NLP) has evolved rapidly, enabling machines to understand and process human language with remarkable accuracy. One crucial aspect of NLP is similarity search and ranking, which is widely used in applications such as search engines, recommendation systems, and question-answering models.
NLP is growing faster than the stock market on a bull run 😅. So if you're thinking about where to invest next - invest (your time) here. With a flood of new concepts, models, and architectures entering the scene every other week, it’s easy to feel overwhelmed.
To efficiently compare and rank textual data, two primary architectures are commonly used: Bi-Encoders and Cross-Encoders. Both play a critical role in tasks like semantic search, text classification, and ranking, but they have distinct trade-offs in terms of speed, accuracy, and computational cost.
In this blog, we will explore what Bi-Encoders and Cross-Encoders are, how they work, their advantages and disadvantages, and when to use each approach. I will also provide code examples to help you implement them in real-world applications.
Why on Earth Do We Even Need This?
What is a Bi-Encoder?
How Bi-Encoders Work
Advantages of Bi-Encoders
Disadvantages of Bi-Encoders
Use Cases of Bi-Encoders
What is a Cross-Encoder?
How Cross-Encoders Work
Advantages of Cross-Encoders
Disadvantages of Cross-Encoders
Use Cases of Cross-Encoders
Key Differences Between Bi-Encoder and Cross-Encoder
When to Use Bi-Encoder vs. Cross-Encoder?
Use a Bi-Encoder When
Use a Cross-Encoder When
Using a Hybrid Approach (Best of Both Worlds)
Code
Final Thoughts: Bi-Encoder vs. Cross-Encoder
1. Why on Earth Do We Even Need This?
Before Bi-Encoders and Cross-Encoders strutted onto the NLP stage like rockstars, comparing the similarity between two texts was… let’s just say, not ideal.
Back in the day, people relied on traditional methods like TF-IDF (Term Frequency–Inverse Document Frequency) or BM25 to measure text relevance. These methods treated text like a bag of words—literally. No word order, no deep context, no idea that “apple” the fruit and “Apple” the company are not the same thing. They did the job (sort of), but they lacked semantic understanding.
Then came the age of neural networks and pre-trained models like BERT, which understood context, syntax, and even sarcasm (kinda). But using BERT directly for everything was computationally expensive—like trying to use a rocket to deliver pizza.
That’s where Bi-Encoders and Cross-Encoders stepped in.
Bi-Encoders said: "What if we process texts separately, turn them into embeddings, and compare vectors? Fast, scalable, and perfect for large datasets."
Cross-Encoders replied: "Cool idea, but what if we look at both texts together and model their deep relationship? Slower, yes—but much more precise."
Together, they gave us the best of both worlds: scalable semantic search with Bi-Encoders and accurate re-ranking with Cross-Encoders.
So yes, we absolutely needed this. And if you’ve ever typed a vague question into Google and still gotten the right answer—you’ve likely benefited from them too.
2. What is a Bi-Encoder?
A Bi-Encoder is a type of neural network architecture used for computing text similarity and retrieval tasks efficiently. It consists of two independent encoders (often based on transformer models like BERT) that process input texts separately and generate fixed-size embeddings. These embeddings can then be compared using a similarity metric such as cosine similarity or dot product.
How Bi-Encoders Work
Each input text (e.g., a query and a document) is passed through the same encoder model separately.
The encoder converts each text into a dense vector representation (embedding).
Not sure what a Dense Vector Representation means?
A dense vector is a compact numerical representation where most values are non-zero. It's typically generated by neural networks like BERT and captures rich semantic meaning—so it understands not just what words appear, but what they mean in context. In contrast, a sparse vector is much larger and mostly filled with zeros. It's usually based on older methods like TF-IDF or Bag-of-Words, which simply track word frequency or presence without any deep understanding of the text. While dense vectors excel at capturing nuanced relationships and meaning, sparse vectors are faster and easier to compute—especially in large-scale, traditional information retrieval systems.
That’s why, in many real-world applications, teams don’t pick one over the other—they combine them. By leveraging the synergy between dense and sparse vectors, hybrid models can achieve both speed and semantic understanding. For example, sparse methods can quickly narrow down candidate documents, while dense vectors can then be used to re-rank them based on meaning. This balanced approach offers the best of both worlds: efficiency and intelligence.
The similarity between the embeddings is computed using a predefined metric.
The most relevant document is retrieved or ranked based on the similarity score.
Example Architecture
Given two input texts:
Query: "What is the capital of France?" Document: "Paris is the capital of France."The Bi-Encoder processes them independently:
Embedding(query) : Encoder("What is the capital of France?") Embedding(document) : Encoder("Paris is the capital of France.")The similarity between these embeddings determines relevance.
Advantages of Bi-Encoders
Efficient for large-scale retrieval – Embeddings can be precomputed and stored in a vector database for fast lookup.
Scalable – Once indexed, queries can be processed in constant time (O(1) retrieval).
Parallel Processing – Documents and queries are encoded separately, making it well-suited for distributed systems.
Disadvantages of Bi-Encoders
Lower accuracy compared to Cross-Encoders – Since interactions between words in both texts are not explicitly modeled, it may miss subtle relationships.
Fixed-length embeddings may lose information – Some context can be lost when compressing text into a single vector.
Use Cases of Bi-Encoders
Semantic Search (e.g., retrieving relevant documents from a database)
Information Retrieval (e.g., search engines, question-answering systems)
Large-scale Similarity Matching (e.g., recommendation systems)
3. What is a Cross-Encoder?
A Cross-Encoder is a type of neural network that processes two input texts together, allowing it to model deep interactions between words in both texts. Unlike Bi-Encoders, which encode each text separately, a Cross-Encoder concatenates the input texts and passes them through the model at the same time. This makes it more accurate but computationally expensive.
How Cross-Encoders Work
The query and document are concatenated into a single input sequence:
[CLS] Query text [SEP] Document text [SEP]The combined sequence is passed through a transformer model (e.g., BERT).
The model outputs a relevance score or classification label, rather than separate embeddings.
Example Architecture
Given a query and a document:
Query:"What is the capital of France?"Document:"Paris is the capital of France."The Cross-Encoder processes them together:
Score=Encoder("[CLS] What is the capital of France? [SEP] Paris is the capital of France. [SEP]")The model outputs a single score representing how well the document matches the query.
Advantages of Cross-Encoders
Higher accuracy – The model considers word-to-word interactions across both texts, leading to better relevance predictions.
Better ranking performance – Ideal for tasks like re-ranking search results where precision is critical.
Disadvantages of Cross-Encoders
Computationally expensive – Requires O(N) complexity since each query-document pair must be processed separately.
Not scalable for large-scale retrieval – Since embeddings cannot be precomputed, it is slow when handling large datasets.
Use Cases of Cross-Encoders
Re-ranking search results (e.g., after initial retrieval by a Bi-Encoder)
Text classification (e.g., Natural Language Inference tasks)
Passage relevance scoring (e.g., in QA systems like Google Search)
4. Key Differences Between Bi-Encoder and Cross-Encoder
Both Bi-Encoders and Cross-Encoders are used for text similarity and ranking tasks, but they have distinct trade-offs in terms of speed, accuracy, and scalability. Below is a detailed comparison:
5. When to Use Bi-Encoder vs. Cross-Encoder?
Choosing between Bi-Encoders and Cross-Encoders depends on the specific task, dataset size, and computational resources. Here’s a breakdown of when to use each approach:
Use a Bi-Encoder When:
You need fast retrieval from a large database (e.g., search engines).
You want to precompute embeddings and store them in a vector database.
Your task involves semantic similarity or matching across a massive dataset.
You have limited computing power and need a scalable solution.
Examples:
Semantic search (e.g., finding similar documents in a knowledge base).
Information retrieval (e.g., initial candidate selection in QA systems).
Recommendation systems (e.g., matching users with relevant products).
Use a Cross-Encoder When:
You need high accuracy and can afford extra computation time.
Your task requires precise ranking of a small set of results.
Your use case involves classification or natural language inference (NLI).
You are working with a small dataset, where speed is less critical.
Examples:
Re-ranking search results (e.g., improving the relevance of top search results).
Text classification (e.g., determining if two texts contradict each other).
Passage ranking (e.g., ranking candidate answers in QA systems).
Using a Hybrid Approach (Best of Both Worlds)
A common strategy is to combine Bi-Encoders and Cross-Encoders for optimal performance:
Bi-Encoder for Retrieval: Quickly fetch the top-k most relevant documents.
Cross-Encoder for Re-Ranking: Re-evaluate the top-k results to refine ranking.
Example Use Case: Search Engine
A Bi-Encoder retrieves the top 100 documents from millions.
A Cross-Encoder re-ranks these 100 documents to ensure the best match is at the top.
This hybrid approach balances speed and accuracy while keeping computations feasible.
6. Code
https://www.kaggle.com/code/dixittrivedi/code-bi-encoder-vs-cross-encoder
7. Final Thoughts: Bi-Encoder vs. Cross-Encoder
Bi-Encoders and Cross-Encoders are both powerful tools in NLP, but they’re good at different things.
Bi-Encoders are great when you need speed. They work well with large datasets and can quickly find similar texts. They’re perfect for search engines and recommendation systems where fast results matter.
Cross-Encoders are better when you need accuracy. They take more time but do a deeper comparison between texts. They’re useful when you want the best possible match, like ranking answers or doing text classification.
The best part? You don’t always have to pick one. Many real-world systems use both: Bi-Encoders to get fast, rough results and Cross-Encoders to fine-tune and improve them.