Ch 12 Unsupervised Training with Query Generation — GenQApply

Inventing the queries you don't have by asking a generative model.

Core concepts

  • The idea. You have passages but no queries. Use a query-generation model (e.g. T5) to synthesize a plausible question for each passage.
  • Synthetic pairs. Each (generated query, passage) becomes a positive training pair — fed to MNR loss to fine-tune the retriever.
  • Asymmetric adaptation. Purpose-built for the query↔passage asymmetry of real search.
  • Noise tolerance. Generated queries are imperfect, but in-batch negatives make training robust to some noise.

What you must master

  • Explain the passage→synthetic-query→pair pipeline Level 1
  • Generate synthetic queries and fine-tune a retriever with them Level 2
  • Assess synthetic-query quality and its effect on retrieval Level 2

Architect’s lens

GenQ is often the fastest route to a domain-adapted retriever when you have documents and an LLM but no query logs. It’s the embedding-world version of synthetic-data bootstrapping — a technique you’ll propose whenever labelled interaction data is missing at project kickoff.