Routing End User Queries to Enterprise Databases
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918309024235520 |
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| author | Sudarshan, Saikrishna Kulkarni, Tanay Patwardhan, Manasi Vig, Lovekesh Srinivasan, Ashwin Verlekar, Tanmay Tulsidas |
| author_facet | Sudarshan, Saikrishna Kulkarni, Tanay Patwardhan, Manasi Vig, Lovekesh Srinivasan, Ashwin Verlekar, Tanmay Tulsidas |
| contents | We address the task of routing natural language queries in multi-database enterprise environments. We construct realistic benchmarks by extending existing NL-to-SQL datasets. Our study shows that routing becomes increasingly challenging with larger, domain-overlapping DB repositories and ambiguous queries, motivating the need for more structured and robust reasoning-based solutions. By explicitly modelling schema coverage, structural connectivity, and fine-grained semantic alignment, the proposed modular, reasoning-driven reranking strategy consistently outperforms embedding-only and direct LLM-prompting baselines across all the metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19825 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Routing End User Queries to Enterprise Databases Sudarshan, Saikrishna Kulkarni, Tanay Patwardhan, Manasi Vig, Lovekesh Srinivasan, Ashwin Verlekar, Tanmay Tulsidas Artificial Intelligence Databases H.2.4; I.2.7; H.3.3 We address the task of routing natural language queries in multi-database enterprise environments. We construct realistic benchmarks by extending existing NL-to-SQL datasets. Our study shows that routing becomes increasingly challenging with larger, domain-overlapping DB repositories and ambiguous queries, motivating the need for more structured and robust reasoning-based solutions. By explicitly modelling schema coverage, structural connectivity, and fine-grained semantic alignment, the proposed modular, reasoning-driven reranking strategy consistently outperforms embedding-only and direct LLM-prompting baselines across all the metrics. |
| title | Routing End User Queries to Enterprise Databases |
| topic | Artificial Intelligence Databases H.2.4; I.2.7; H.3.3 |
| url | https://arxiv.org/abs/2601.19825 |