Cross-Encoder

• Reranker

  Second-stage model re-scoring a candidate set retrieved by first-stage retrieval; improves ranking quality at modest computational cost.

• Knowledge Distillation for IR

  Training a fast bi-encoder (student) to mimic the ranking scores of a slow cross-encoder (teacher); the dominant approach for improving dense retrieval without cross-encoder latency.

• MonoBERT

  BERT-based pointwise reranker that concatenates query and passage for joint encoding; the standard baseline for neural reranking on MS MARCO.

• MonoT5

  T5-based pointwise reranker that generates “true”/“false” tokens to score relevance; more efficient than MonoBERT and generalizes well across domains.

• Cross-Encoder

  Neural architecture jointly encoding query-document pairs for accurate relevance scoring; used for reranking retrieved candidates from first-stage retrieval.