It's All Relative! -- A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction

Understand

Recent developments in large language models (LLMs) have shown promise in their ability to generate synthetic query-document pairs by prompting with as few as 8 demonstrations.

• This has enabled building better IR models, especially for tasks with no training data readily available.
• Typically, such synthetic query generation (QGen) approaches condition on an input context (e.g.
• a text document) and generate a query relevant to that context, or condition the QGen model additionally on the relevance label (e.g.