Listwise-Ranking

• Listwise Ranking Loss

  Ranking loss functions that optimize over the entire ranked list rather than individual pairs or points; includes LambdaLoss, ListNet, ApproxNDCG, and softmax cross-entropy.

• 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.

• RankT5

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