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| Main Authors: | , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.10109 |
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| _version_ | 1866913110738075648 |
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| author | Fujimaki, Haruki Kato, Makoto P. |
| author_facet | Fujimaki, Haruki Kato, Makoto P. |
| contents | This study addresses the challenge of improving dense retrieval performance for queries containing numerical conditions, such as ``companies with more than one billion dollars in R&D expenditure.'' Although recent research has shown that standard models struggle with numeric information in domains such as finance, e-commerce, and medicine, existing solutions typically decompose queries into textual and numerical components and score them separately. These approaches modify late-interaction retrieval models such as ColBERT and introduce challenges in deployment, latency, and maintainability. To overcome these limitations, we propose NumColBERT, an inference-time non-intrusive method that enhances numerically conditioned retrieval while preserving the original late-interaction mechanism. Because NumColBERT retains the standard ColBERT indexing and MaxSim scoring pipeline, existing optimizations and ecosystem components can be reused directly, facilitating practical deployment. NumColBERT introduces a Numerical Gating Mechanism and a Numerical Contrastive Learning objective to enable numerical conditions to contribute more effectively within standard ColBERT scoring. The gating mechanism amplifies tokens carrying critical numerical constraints while suppressing context-neutral numerical mentions, and the contrastive objective shapes the embedding space to reflect numerical magnitudes, units, and conditions. Experimental results show that NumColBERT substantially outperforms standard fine-tuning baselines and achieves accuracy comparable to or better than prior approaches relying on separate textual and numerical scoring. These findings demonstrate the feasibility of numerically conditioned retrieval with a non-intrusive inference pipeline and present a maintainable solution for real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10109 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NumColBERT: Non-Intrusive Numeracy Injection for Late-Interaction Retrieval Models Fujimaki, Haruki Kato, Makoto P. Information Retrieval This study addresses the challenge of improving dense retrieval performance for queries containing numerical conditions, such as ``companies with more than one billion dollars in R&D expenditure.'' Although recent research has shown that standard models struggle with numeric information in domains such as finance, e-commerce, and medicine, existing solutions typically decompose queries into textual and numerical components and score them separately. These approaches modify late-interaction retrieval models such as ColBERT and introduce challenges in deployment, latency, and maintainability. To overcome these limitations, we propose NumColBERT, an inference-time non-intrusive method that enhances numerically conditioned retrieval while preserving the original late-interaction mechanism. Because NumColBERT retains the standard ColBERT indexing and MaxSim scoring pipeline, existing optimizations and ecosystem components can be reused directly, facilitating practical deployment. NumColBERT introduces a Numerical Gating Mechanism and a Numerical Contrastive Learning objective to enable numerical conditions to contribute more effectively within standard ColBERT scoring. The gating mechanism amplifies tokens carrying critical numerical constraints while suppressing context-neutral numerical mentions, and the contrastive objective shapes the embedding space to reflect numerical magnitudes, units, and conditions. Experimental results show that NumColBERT substantially outperforms standard fine-tuning baselines and achieves accuracy comparable to or better than prior approaches relying on separate textual and numerical scoring. These findings demonstrate the feasibility of numerically conditioned retrieval with a non-intrusive inference pipeline and present a maintainable solution for real-world deployment. |
| title | NumColBERT: Non-Intrusive Numeracy Injection for Late-Interaction Retrieval Models |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2605.10109 |