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ML-Powered Search & Ranking • Dense Retrieval (BERT-based Embeddings)Easy⏱️ ~3 min

What is Dense Retrieval with BERT Based Embeddings?

Definition
Dense retrieval finds relevant documents by computing similarity between learned vector representations (embeddings) of queries and documents. Unlike keyword matching, it captures semantic meaning: "automobile" matches "car" even without shared words.
How It Works
Both queries and documents are encoded into fixed-length vectors (typically 768 dimensions for BERT-based models, 384-512 for efficient variants). Relevance is computed as dot product or cosine similarity between vectors. Documents are pre-indexed; at query time, encode the query and find nearest neighbors. The key insight: semantically similar texts cluster in vector space even without lexical overlap.
Dense vs Sparse Retrieval
Sparse (BM25, TF-IDF): Exact keyword matching. Fast, interpretable, handles rare terms well. Fails on synonyms, paraphrases, and semantic similarity. Dense: Semantic matching through learned embeddings. Handles synonyms and paraphrases. Struggles with rare/specific terms, exact matches, and requires training data. Neither dominates; production systems often combine both.
When Dense Retrieval Shines
Best for: semantic search where vocabulary varies ("inexpensive" vs "cheap"), question answering (question and answer rarely share words), multilingual search (embeddings bridge languages). Improves recall by 10-30% over sparse methods for semantic queries. Not ideal for: exact entity matching, code search, or domains with specialized vocabulary lacking training data.

💡 Key Takeaways

✓Dense retrieval uses learned vector embeddings to capture semantic similarity without shared words

✓Vectors are typically 768 dimensions (BERT) or 384-512 (efficient models); similarity via dot product

✓Sparse (BM25): exact keywords, fast, handles rare terms; Dense: semantic, handles synonyms

✓Dense improves recall 10-30% for semantic queries but struggles with exact entity matching

✓Best for: semantic search, QA, multilingual; combine with sparse for production

📌 Interview Tips

1Explain dense vs sparse trade-offs (synonyms vs rare terms) as a comparison framework

2Mention typical embedding dimensions (768 for BERT, 384-512 for efficient) for technical depth

3Cite 10-30% recall improvement for semantic queries to quantify the benefit

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