LIR: The First Workshop on Late Interaction and Multi Vector Retrieval @ ECIR 2026
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Abstract (arXiv)
Late interaction retrieval methods, pioneered by ColBERT, have emerged as a powerful alternative to single-vector neural IR. By leveraging fine-grained, token-level representations, they have been demonstrated to deliver strong generalisation and robustness, particularly in out-of-domain settings. They have recently been shown to be particularly well-suited for novel use cases, such as reasoning-based or cross-modality retrieval. At the same time, these models pose significant challenges of efficiency, usability, and integrations into fully fledged systems; as well as the natural difficulties encountered while researching novel application domains. Recent years have seen rapid advances across many of these areas, but research efforts remain fragmented across communities and frequently exclude practitioners. The purpose of this workshop is to create an environment where all aspects of late interaction can be discussed, with a focus on early research explorations, real-world outcomes, and negative or puzzling results to be freely shared and discussed. The aim of LIR is to provide a highly-interactive environment for researchers from various backgrounds and practitioners to freely discuss their experience, fostering further collaboration.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 41 references that are in the map. First posted on arXiv 1 Nov 2025.
Builds on
32 of 41 references.
References · 32
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
- 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
- 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
- 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
- 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 554
- 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 476
- 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 376
- 2021SPLADE: Sparse Lexical and Expansion Model for First Stage Rankingcited by 286
- 2025BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agentcited by 200
- 2024ColPali: Efficient Document Retrieval with Vision Language Modelscited by 195
- 2025On the Theoretical Limitations of Embedding-Based Retrievalcited by 104
20 more
- 2022PLAID: An Efficient Engine for Late Interaction Retrievalcited by 101
- 2025ReasonIR: Training Retrievers for Reasoning Taskscited by 77
- 2024FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructionscited by 69
- 2022Transfer Learning Approaches for Building Cross-Language Dense Retrieval Modelscited by 36
- 2024MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodingscited by 32
- 2023Rethinking the Role of Token Retrieval in Multi-Vector Retrievalcited by 31
- 2025Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddingscited by 26
- 2024Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retrievercited by 25
- 2024Reducing the Footprint of Multi-Vector Retrieval with Minimal Performance Impact via Token Poolingcited by 25
- 2025Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrievalcited by 20
- 2025PyLate: Flexible Training and Retrieval for Late Interaction Modelscited by 18
- 2025WARP: An Efficient Engine for Multi-Vector Retrievalcited by 17
- 2024JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resourcescited by 15
- 2025Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddingscited by 13
- 2025Efficient Constant-Space Multi-Vector Retrievalcited by 12
- 2025CRISP: Clustering Multi-Vector Representations for Denoising and Pruningcited by 8
- 2024ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrievalcited by 7
- 2025Hypencoder: Hypernetworks for Information Retrievalcited by 7
- 2024Efficient Document Ranking with Learnable Late Interactionscited by 6
- 2025Robust-IR @ SIGIR 2025: The First Workshop on Robust Information Retrievalcited by 1
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Citing papers per year: 2026: 1.
1 paper.