Pylate: Flexible Training And Retrieval For Late Interaction Models

Abstract

Neural ranking has become a cornerstone of modern information retrieval. While single vector search remains the dominant paradigm, it suffers from the shortcoming of compressing all the information into a single vector. This compression leads to notable performance degradation in out-of-domain, long-context, and reasoning-intensive retrieval tasks. Multi-vector approaches pioneered by ColBERT aim to address these limitations by preserving individual token embeddings and computing similarity via the MaxSim operator. This architecture has demonstrated superior empirical advantages, including enhanced out-of-domain generalization, long-context handling, and performance in complex retrieval scenarios. Despite these compelling empirical results and clear theoretical advantages, the practical adoption and public availability of late interaction models remain low compared to their single-vector counterparts, primarily due to a lack of accessible and modular tools for training and experimentin

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keychaffin2025pylate

Related papers

• Colbertv2: Effective And Efficient Retrieval Via Lightweight Late Interaction (2021)17.46
• SLIM: Sparsified Late Interaction For Multi-vector Retrieval With Inverted Indexes (2023)7.50
• Colbert-att: Late-interaction Meets Attention For Enhanced Retrieval (2026)0.00
• Colbert: Efficient And Effective Passage Search Via Contextualized Late Interaction Over BERT (2020)0.00
• Introducing Neural Bag Of Whole-words With Colberter: Contextualized Late Interactions Using Enhanced Reduction (2022)0.00
• Jina-colbert-v2: A General-purpose Multilingual Late Interaction Retriever (2024)5.24
• Working Notes On Late Interaction Dynamics: Analyzing Targeted Behaviors Of Late Interaction Models (2026)0.00
• Developing Visual Augmented Q&A System Using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker (2025)0.00