jina-reranker-v3: Last but Not Late Interaction for Document Reranking
jina-reranker-v3 is a 0.6B parameter multilingual document reranker that
introduces a novel last but not late interaction. Unlike late interaction
models such as ColBERT that perform separate encoding followed by multi-vector
matching, our approach conducts causal self-attention between query and
documents within the same context window, enabling rich cross-document
interactions before extracting contextual embeddings from the last token of
each document. This compact architecture achieves state-of-the-art BEIR
performance with 61.94 nDCG@10 while being ten times smaller than generative
listwise rerankers.
68dd0458e795697646c0a04b
Feng Wang
68dd0458e795697646c0a04c
Yuqing Li
68dd0458e795697646c0a04d
Han Xiao
claimed_verified
2025-10-01T15:29:11.728Z
603763514de52ff951d89793
https://cdn-avatars.huggingface.co/v1/production/uploads/603763514de52ff951d89793/n-QouGYg7oE5QeDaAb3Ns.png
Han Xiao
user
hanxiao
jina-reranker-v3: Last but Not Late Interaction for Document Reranking
jina-reranker-v3 is a 0.6B parameter multilingual document reranker that
introduces a novel last but not late interaction. Unlike late interaction
models such as ColBERT that perform separate encoding followed by multi-vector
matching, our approach conducts causal self-attention between query and
documents within the same context window, enabling rich cross-document
interactions before extracting contextual embeddings from the last token of
each document. This compact architecture achieves state-of-the-art BEIR
performance with 61.94 nDCG@10 while being ten times smaller than generative
listwise rerankers.
A multilingual document reranker using causal self-attention achieves state-of-the-art performance with a compact architecture.
multilingual document reranker causal self-attention BEIR nDCG@10 generative listwise rerankers