🔎 A Deep Dive into Cross-Encoders and How They Work

1️⃣ Introduction

In AI systems that retrieve or generate information, ranking quality and relevance are critical. Whether you are building a RAG-based assistant, a knowledge-driven chatbot, or a classical search engine, users expect the most accurate, contextually appropriate, and useful answers to appear first.

Traditional retrieval methods, such as keyword-based search (BM25) or bi-encoder embeddings, can capture some relevant results but often miss subtle relationships, domain-specific phrasing, or nuanced context cues. Cross-encoders address this gap by jointly encoding query–document pairs, allowing token-level interactions that improve precision, contextual understanding, and alignment with human judgment.

They are particularly valuable when accuracy is paramount, for instance:

• Re-ranking candidate documents in large retrieval pipelines
• Selecting the most relevant context for RAG-based assistants
• Handling domain-specific queries in healthcare, legal, or technical applications

What You Will Learn

• How cross-encoders work and why they outperform BM25 or bi-encoders.
• How to construct query–document inputs and perform joint transformer encoding.
• How to score relevance using the [CLS] embedding via a linear layer or MLP.
• How to implement cross-encoder scoring and re-ranking in Python.
• How to combine fast retrieval methods (BM25/bi-encoders) with cross-encoder re-ranking.
• Examples of real-world applications.

This article will guide you through the inner workings, practical implementations, and best practices for cross-encoders, giving you a solid foundation to integrate them effectively into both retrieval and generation pipelines.

2️⃣ What Are Cross-Encoders?

A cross-encoder is a transformer model that takes a query and a document (or passage) together as input and produces a relevance score.

Unlike bi-encoders, which encode queries and documents independently and rely on vector similarity, cross-encoders allow full cross-attention between the query and document tokens. This enables the model to:

• Capture subtle semantic nuances
• Understand negations, comparisons, or cause-effect relationships
• Rank answers more accurately in both retrieval and generation settings

Input format example:

[CLS] Query Text [SEP] Document Text [SEP]

The [CLS] token embedding is passed through a classification or regression head to compute the relevance score.

3️⃣ Why Cross-Encoders Matter in Both RAG and Classical Search

✅ Advantages:

• Precision & Context Awareness: Capture nuanced relationships between query and content.
• Alignment with Human Judgment: Produces results that feel natural and accurate to users.
• Domain Adaptation: Fine-tunable for any domain (legal, medical, technical, environmental).

⚠️ Trade-offs:

• Computationally expensive since each query–document pair is processed jointly.
• Not ideal for very large-scale retrieval on its own — best used as a re-ranker after a fast retrieval stage (BM25, bi-encoder, or other dense retrieval).

4️⃣ How Cross-Encoders Work

Step 1: Input Construction

A query and a candidate document are combined into a single input sequence for the transformer.

[CLS] "Best practices for recycling lithium-ion batteries" [SEP] 
"Lithium-ion batteries should be processed with thermal pre-treatment to reduce hazards." [SEP]

Step 2: Transformer Encoding (Joint)

The model processes this sequence, allowing cross-attention between query and document tokens.

• The query word “recycling” can directly attend to document words like “processed” and “reduce hazards”.
• The model learns fine-grained relationships.

Step 3: Relevance Scoring

The final [CLS] token embedding is passed through a classification or regression head to produce a relevance score (e.g., 0.0–1.0).

Following diagram depicts the above steps:

5️⃣ Why Use Cross-Encoders?

✅ Precision → Capture subtle differences like negations, comparisons, cause-effect.
✅ Contextual Matching → Understand domain-specific queries and rare terminology.
✅ Human-Like Judgment → Often align better with human rankings than other methods.

⚠️ Trade-Off: Expensive. They require joint inference per query–document pair, making them unsuitable for very large-scale retrieval directly.

6️⃣ Cross-Encoders in a Retrieval Pipeline

Since cross-encoders are slow, they are typically used as re-rankers:

1. Candidate Retrieval (fast)
   • Use BM25 or a bi-encoder to retrieve top-k candidates.
2. Re-Ranking (precise)
   • Apply a cross-encoder only to those candidates.
3. Final Results
   • Highly relevant docs surface at the top.

7️⃣ Python Examples: Scoring with a Cross-Encoder

7.1 Scoring with a Cross-Encoder

pip install sentence-transformers

from sentence_transformers import CrossEncoder

# Load a pre-trained cross-encoder model
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

# Query and documents
query = "Best practices for recycling lithium-ion batteries"
documents = [
    "Lithium-ion batteries should be processed with thermal pre-treatment to reduce hazards.",
    "Wind turbines generate clean energy in coastal regions.",
    "Battery recycling reduces environmental footprint significantly."
]

# Create pairs
pairs = [(query, doc) for doc in documents]

# Predict relevance scores
scores = model.predict(pairs)

for doc, score in zip(documents, scores):
    print(f"Score: {score:.4f} | {doc}")

Score: 0.4742 | Lithium-ion batteries should be processed with thermal pre-treatment to reduce hazards.
Score: -11.2687 | Wind turbines generate clean energy in coastal regions.
Score: -0.7598 | Battery recycling reduces environmental footprint significantly.

👉 Output shows recycling-related docs with higher scores than irrelevant ones.

7.2 Cross-Encoder as Re-Ranker

pip install rank-bm25 sentence-transformers

from rank_bm25 import BM25Okapi
from sentence_transformers import CrossEncoder

# Candidate documents
corpus = [
    "Wind turbines increase electricity generation capacity in coastal regions.",
    "Battery recycling reduces lifecycle carbon footprint of EVs.",
    "Hydrogen electrolyzers are becoming more efficient in Japan.",
]

# Step 1: BM25 Retrieval
tokenized_corpus = [doc.split(" ") for doc in corpus]
bm25 = BM25Okapi(tokenized_corpus)

query = "Efficiency of hydrogen electrolyzers"
bm25_scores = bm25.get_scores(query.split(" "))

# Select top candidates
top_docs = [corpus[i] for i in bm25_scores.argsort()[-2:][::-1]]

# Step 2: Cross-Encoder Re-ranking
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
pairs = [(query, doc) for doc in top_docs]
rerank_scores = model.predict(pairs)

print("nFinal Ranked Results:")
for doc, score in sorted(zip(top_docs, rerank_scores), key=lambda x: x[1], reverse=True):
    print(f"{score:.4f} | {doc}")

Final Ranked Results:
5.5779 | Hydrogen electrolyzers are becoming more efficient in Japan.
-11.3173 | Battery recycling reduces lifecycle carbon footprint of EVs.

Here, BM25 gives a rough shortlist, and the cross-encoder ensures true relevance comes first.

8️⃣ Real-World Applications

• Search Engines → Re-ranking documents for more precise results
• Legal & Policy Research → Matching queries to exact statutes/clauses
• Healthcare AI → Ranking medical literature for clinical questions
• Customer Support → Matching troubleshooting queries to correct FAQ entries
• E-commerce → Ranking products based on nuanced query matches

9️⃣ Strengths vs. Limitations

🔟Bi-Encoder vs Cross-Encoder Architecture

Figure: Bi-Encoder vs Cross-Encoder

💡Conclusion

Cross-encoders are the precision workhorses of modern information retrieval.
They are not designed to scale across millions of documents alone, but as re-rankers, they deliver results that feel much closer to human judgment.

If you’re building any system where accuracy is critical — from search engines to knowledge assistants — a cross-encoder should be part of your stack.

📚 References & Resources

• SentenceTransformers: Cross-Encoders
• HuggingFace Cross-Encoder Models