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English(EN) 77.1% on BIRD dev with one model call per question, and a test-set submission in

Claude Opus 5.5 在 BIRD 文本到 SQL 基准测试中以单次查询达到 77.1%

一位开发者使用 Claude Opus 5.5,通过每次调用模型回答一个问题的方式,在 BIRD 文本到 SQL 基准测试中实现了 77.1% 的执行准确率。该方法通过 schemagate 系统实现,涉及模型选择相关表并生成单个查询,并包含一个机制来修复 SQLite 遇到的错误。开发者正在将这些结果提交到隐藏测试集,以确定该系统与其他通常每次问题使用多次调用的领先方法相比的性能。该系统在访问控制基准测试中也表现强劲,实现了零未授权读取。 AI

影响 展示了文本到 SQL 任务中高效的 LLM 查询方式,可能降低复杂数据分析的计算成本。

排序理由 该项目详细介绍了使用特定 LLM (Claude Opus 5.5) 在特定学术基准测试 (BIRD 文本到 SQL) 上的性能,属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

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Claude Opus 5.5 在 BIRD 文本到 SQL 基准测试中以单次查询达到 77.1%

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该项目详细介绍了使用特定 LLM (Claude Opus 5.5) 在特定学术基准测试 (BIRD 文本到 SQL) 上的性能,属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]
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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.
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model release, product
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High
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1 days old
Coverage has settled into its steady-state source set.

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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Ashish sinha ·

    77.1% on BIRD dev with one model call per question, and a test-set submission in

    <p><a href="https://bird-bench.github.io/" rel="noopener noreferrer">BIRD</a> is the text-to-SQL benchmark the serious systems compete on: 1,534 dev questions over 11 real databases, scored by execution accuracy, with a hidden test set that only the BIRD team can run. I ran <a hr…