Reranking

• LLM Rerankers (RankGPT)

  Zero-shot document reranking using large language models prompted to produce a relevance-ordered permutation of candidate passages; no fine-tuning required.

• 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.

• RankT5

  T5-based listwise reranker that directly optimizes ranking metrics by generating ordered document IDs; addresses exposure bias in pointwise and pairwise approaches.

• Two-Stage Retrieval

  Retrieve-then-rerank pipeline where a fast first-stage retriever (BM25 or bi-encoder) produces a candidate set, which a slower but more accurate reranker (cross-encoder) then orders.

• Cross-Encoder

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