Cross-Encoder Reranking

Cross-Encoder Reranking is a two-stage information retrieval technique that evaluates the conceptual relevance of a query against candidate documents. It uses a deep learning model to process the query and document together, producing a highly accurate similarity score.

How it Works

Standard retrieval systems use bi-encoders to search vectors independently for speed. Reranking applies a second, more powerful filter over a smaller subset of results.

• Joint Input Encoding: The cross-encoder model processes the user query and candidate document chunk simultaneously, allowing full self-attention across both texts.
• Attention Scoring: Evaluates the relationships between every token in the query and every token in the document, determining contextual relevance.
• Filtering Stage: Sorts the top candidates (e.g., the top 100) from the initial search, passing only the highest-scoring chunks to the LLM.

Lakehouse & Agentic Relevance

In a data lakehouse, retrieval pipelines handle vast sets of technical and business documents. Autonomous agents need high-quality grounding data to prevent hallucinations, but feeding too many chunks into the LLM context window causes latency and cost. Cross-encoder reranking solves this by filtering search results down to the most relevant items. Dremio accelerates these pipelines by pre-filtering datasets and supplying structured, schema-compliant tables to the reranking pipelines, optimizing retrieval speed.