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Choose Tokenization and Dense or Sparse Retrieval

C3 AIData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

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This problem tests your understanding of document retrieval strategies, specifically comparing sparse and dense representations and how to integrate them into a production RAG system.

The Scenario

You are building a semantic search engine for a large-scale enterprise document repository (millions of technical PDFs and internal wikis). Your system needs to handle keyword-based queries, specific product identifiers (SKUs), and complex natural language questions.

The Task

  1. Compare and contrast Sparse Retrieval (e.g., BM25/TF-IDF) and Dense Retrieval (e.g., Bi-Encoders using BERT-based embeddings).
  2. Explain how you would handle 'Out-of-Vocabulary' (OOV) issues and specific alphanumeric product identifiers using different tokenization strategies (WordPiece vs. Byte-Pair Encoding).
  3. Propose a hybrid retrieval architecture that leverages both strategies. Describe the fusion mechanism (e.g., Reciprocal Rank Fusion) and how you would balance recall and latency.

Example Case

Suppose a user searches for 'Error code XJ-900 battery failure'.

  • Why might a Dense retriever fail to retrieve the specific manual for 'XJ-900'?
  • How does a Sparse retriever help in this specific instance?
  • How does tokenization affect the representation of 'XJ-900' in each model?

Hints

Hint 1

Consider the limitations of embedding models when encountering rare, task-specific entities that were not well-represented in the pre-training corpus.

Hint 2

Think about how Lexical overlap (exact matches) is captured by Sparse methods and how contextual synonyms are captured by Dense methods.

Hint 3

Consider the trade-offs of using Reciprocal Rank Fusion (RRF) versus a re-ranking model (Cross-Encoder) for combining the results of two different retrieval streams.

Follow-up questions

  • How would you optimize latency if your hybrid system requires performing both a vector search and an inverted index search in parallel?
  • If your system must support 20 different languages, how does your choice of tokenizer and embedding model change?
  • How would you evaluate the performance of your hybrid system if you do not have ground-truth relevance labels (Q&A pairs) initially?