English(EN) Towards Optimizing SQL Generation via LLM Routing

新的LLM路由优化文本到SQL生成，以降低成本和提高速度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新颖的文本到SQL任务LLM路由方法，旨在优化成本和延迟。该方法动态选择最经济高效且能够准确生成SQL查询的LLM，从而降低简单请求的费用，同时保持复杂请求的高准确性。提出的基于分数和基于分类的策略在BIRD数据集上展示了准确性和成本之间的实际权衡，并且路由器的设计考虑了高效的训练和推理。 AI

影响这种方法可以显著降低AI驱动的数据库查询系统的运营成本。

排序理由该集群包含一篇详细介绍LLM性能优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 17 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍LLM性能优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Mohammadhossein Malekpour, Nour Shaheen, Foutse Khomh, Amine Mhedhbi · 2026-09-15 04:00

通过LLM路由优化SQL生成

arXiv:2411.04319v1 Announce Type: cross Abstract: 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 unn…

本文如何被排名

报道来源 [1]

通过LLM路由优化SQL生成

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