arXiv:2601.21853v2 Announce Type: replace-cross Abstract: Multi-vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single-vector representations in information retrieval applications. In multi-vector retrieval systems, both queries and documents are encoded using one embedding per token, and similarity between queries and documents is measured by the MaxSim similarity measure. However, the improved quality of multi-vector retrieval comes at the expense of significantly increased search latency. In this work, we introduce
The proliferation of complex AI models necessitates more efficient and scalable information retrieval methods to handle ever-growing data volumes.
This development addresses a critical bottleneck in leveraging multi-vector representations for information retrieval, directly impacting the performance and applicability of advanced AI systems.
The introduction of LEMUR suggests a method to mitigate the significant search latency associated with superior multi-vector retrieval quality, making these advanced methods more practical for real-world applications.
- · AI application developers
- · Search engine companies
- · Cloud infrastructure providers
- · Generative AI platforms
- · Companies relying on less efficient retrieval architectures
- · Information retrieval startups without competitive latency solutions
Artificial intelligence applications requiring high-quality, low-latency information retrieval will become more performant and accessible.
The improved efficiency could accelerate the development and deployment of more sophisticated AI agents and knowledge management systems.
Enhanced retrieval capabilities might lead to new paradigms in how information is accessed, processed, and utilized across industries, potentially impacting data-driven decision making.
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