Towards Optimizing SQL Generation via LLM Routing
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910687187435520 |
|---|---|
| author | Malekpour, Mohammadhossein Shaheen, Nour Khomh, Foutse Mhedhbi, Amine |
| author_facet | Malekpour, Mohammadhossein Shaheen, Nour Khomh, Foutse Mhedhbi, Amine |
| contents | Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04319 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Optimizing SQL Generation via LLM Routing Malekpour, Mohammadhossein Shaheen, Nour Khomh, Foutse Mhedhbi, Amine Databases Machine Learning Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset. |
| title | Towards Optimizing SQL Generation via LLM Routing |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2411.04319 |