@inproceedings{sudarshan-etal-2026-routing,
title = "Routing End User Queries to Enterprise Databases",
author = "Sudarshan, Saikrishna and
Kulkarni, Tanay and
Patwardhan, Manasi and
Vig, Lovekesh and
Srinivasan, Ashwin and
Verlekar, Tanmay Tulsidas",
editor = "Gupta, Vivek and
Ding, Kaize and
Kokel, Harsha and
Zhao, Yue and
Agarwal, Amit and
Wang, Yu and
Glass, Michael and
Zhang, Yu and
Srinivas, Kavitha and
Chen, Xiusi and
Hassanzadeh, Oktie and
Zhu, Qi and
Chang, Shuaichen and
Luo, Yuan",
booktitle = "Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the {LLM} Era ({SURG}e{LLM} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.surgellm-1.16/",
pages = "260--268",
ISBN = "979-8-89176-406-4",
abstract = "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 re-ranking strategy consistently outperforms embedding-only and direct LLM-prompting baselines across all the metrics."
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%0 Conference Proceedings
%T Routing End User Queries to Enterprise Databases
%A Sudarshan, Saikrishna
%A Kulkarni, Tanay
%A Patwardhan, Manasi
%A Vig, Lovekesh
%A Srinivasan, Ashwin
%A Verlekar, Tanmay Tulsidas
%Y Gupta, Vivek
%Y Ding, Kaize
%Y Kokel, Harsha
%Y Zhao, Yue
%Y Agarwal, Amit
%Y Wang, Yu
%Y Glass, Michael
%Y Zhang, Yu
%Y Srinivas, Kavitha
%Y Chen, Xiusi
%Y Hassanzadeh, Oktie
%Y Zhu, Qi
%Y Chang, Shuaichen
%Y Luo, Yuan
%S Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-406-4
%F sudarshan-etal-2026-routing
%X 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 re-ranking strategy consistently outperforms embedding-only and direct LLM-prompting baselines across all the metrics.
%U https://aclanthology.org/2026.surgellm-1.16/
%P 260-268
Markdown (Informal)
[Routing End User Queries to Enterprise Databases](https://aclanthology.org/2026.surgellm-1.16/) (Sudarshan et al., SURGeLLM 2026)
ACL
- Saikrishna Sudarshan, Tanay Kulkarni, Manasi Patwardhan, Lovekesh Vig, Ashwin Srinivasan, and Tanmay Tulsidas Verlekar. 2026. Routing End User Queries to Enterprise Databases. In Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026), pages 260–268, San Diego, California, United States. Association for Computational Linguistics.