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English(EN) An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data

新工具FusionSQL在无标签数据下评估Text2SQL模型

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新研究论文介绍了一种名为FusionSQL的评估工具，旨在评估Text2SQL模型在未见和无标签数据集上的表现。该方法分析模型自身输出来识别模式，从而估算准确性，解决了因数据库演变或隐私问题而无法获得已验证答案的关键部署挑战。FusionSQL旨在实现Text2SQL系统质量的预发布检查和持续监控。 AI

影响 通过提供一种评估无标签数据上性能的方法，实现了Text2SQL系统更高效可靠的部署。

排序理由 该集群包含一篇详细介绍AI模型新评估方法的 ist research paper。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新工具FusionSQL在无标签数据下评估Text2SQL模型

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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Trinh Pham, Thanh Tam Nguyen, Viet Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen ·

    面向未见和无标签数据的Text2SQL模型的高效有效评估器

    arXiv:2603.07841v2 Announce Type: replace Abstract: Recent advances in large language models have strengthened Text2SQL systems that translate natural language questions into database queries. A persistent deployment challenge is to assess a newly trained Text2SQL system on an un…

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