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English(EN) CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation

新的CA-SQL系统提高了LLM在复杂查询上的文本到SQL准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了CA-SQL，一个旨在提高大型语言模型在复杂数据库查询准确性的新型文本到SQL系统。CA-SQL根据查询的估计难度动态调整其潜在解决方案的搜索，采用一种新颖的提示种子方法和投票机制来选择最佳候选。该方法仅使用GPT-4o-mini在具有挑战性的BIRD基准测试层级上取得了51.72%的最新分数，优于更大的模型。 AI

影响 增强了LLM在复杂数据库查询方面的能力，可能改进数据分析工具。

排序理由 该集群包含一篇详细介绍文本到SQL任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的CA-SQL系统提高了LLM在复杂查询上的文本到SQL准确性

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该集群包含一篇详细介绍文本到SQL任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nianwen Xue ·

    CA-SQL：通过探索和计算预算分配实现文本到SQL的复杂性感知推理时间

    While recent advancements in inference-time learning have improved LLM reasoning on Text-to-SQL tasks, current solutions still struggle to perform well on the most challenging tasks in the Bird-Bench (BIRD) benchmark. This is due to inadequate solution space exploration, which is…

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