Cross-Encoders are the precision instruments of AI search. While bi-encoders (used for initial retrieval) encode queries and documents separately, cross-encoders see both together—enabling deep understanding of how well a document answers a specific query. This is why they’re used in reranking: when precision matters most.
How Cross-Encoders Work
- Joint Input: Query and document are concatenated and fed together into the transformer.
- Full Attention: Every query token can attend to every document token and vice versa.
- Relevance Score: Output is a single score indicating how well the document matches the query.
- No Pre-computation: Unlike bi-encoders, document representations can’t be pre-computed.
Cross-Encoder vs Bi-Encoder
| Aspect | Cross-Encoder | Bi-Encoder |
|---|---|---|
| Input | Query + Doc together | Query and Doc separate |
| Accuracy | Higher | Lower |
| Speed | Slow (can’t pre-compute) | Fast (pre-computed embeddings) |
| Use Case | Reranking top results | Initial retrieval |
| Scale | Top 100-1000 candidates | Millions of documents |
Why Cross-Encoders Matter for AI-SEO
- Final Selection: Cross-encoders often make the final decision about which content gets cited.
- Deep Relevance: They understand nuanced query-document relationships—surface-level relevance isn’t enough.
- Quality Bar: Content must genuinely answer the query, not just be topically related.
- Reranking Stage: Understanding cross-encoders explains why some retrieved content doesn’t make the final cut.
“Cross-encoders ask: does this document actually answer this query? Not just: is it about the same topic? This is the bar your content must clear.”
Optimizing for Cross-Encoder Evaluation
- Direct Answers: Ensure content directly addresses the query intent, not just related topics.
- Query-Content Alignment: Structure content so key answers are easily matched to likely queries.
- Comprehensive Coverage: Cross-encoders can see if important aspects are missing.
- Clear Statements: Explicit, unambiguous claims score better than vague content.
Related Concepts
- Reranking – Where cross-encoders are primarily used
- Embeddings – Bi-encoder output used for initial retrieval
- Transformer – Architecture cross-encoders are built on
Frequently Asked Questions
Computational cost. Cross-encoders must process each query-document pair individually. For a million documents, that’s a million forward passes. Bi-encoders can pre-compute document embeddings, making retrieval much faster. The solution is two stages: fast initial retrieval, then precise cross-encoder reranking.
Cross-encoders typically outperform bi-encoders significantly on relevance benchmarks. The joint encoding allows them to capture subtle relevance signals that separate embeddings miss. This is why they’re the standard for reranking in production systems.
Sources
- Passage Re-ranking with BERT – Nogueira & Cho, 2019
- Sentence Transformers Cross-Encoder Documentation
Future Outlook
Cross-encoders will become more efficient through distillation and optimized architectures. As they become faster, their use may expand beyond reranking. Content that performs well under cross-encoder evaluation will have increasing advantages.