English(EN) ARCS: Towards Precise Text-to-SQL via Structured Disambiguation

新的ARCS基准揭示文本到SQL模型在处理歧义时存在困难

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

开发了一个名为ARCS的新基准来解决文本到SQL系统中的歧义挑战。该基准包含真实世界数据库上的自然歧义，并附带了有效歧义点、解释和相应SQL查询的注释。ARCS的初步实验表明，当前的文本到SQL模型在处理歧义时存在困难，GPT-6 Sol模型的执行准确率仅为51%，开源模型的准确率低于27%。提出的结构化消歧范式旨在通过显式、受约束的交互来解决这些歧义，而不是通过对话澄清。 AI

影响强调了开发能够处理现实世界歧义的健壮文本到SQL系统所面临的重大挑战，可能为该领域的未来研究提供指导。

排序理由该集群描述了一个新的学术基准和相关的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Signal score: 7 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一个新的学术基准和相关的研究论文。[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, other; 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: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Yihao Hu, Yanlin Feng, Naoki Otani, Nikita Bhutani · 2026-10-08 04:00

ARCS：通过结构化消歧实现精确的文本到SQL

arXiv:2610.09396v1 Announce Type: new Abstract: As text-to-SQL systems move beyond demonstrations toward real-world deployment, ambiguity in user questions becomes a primary source of errors. Such ambiguities are often subtle, domain- or data-specific, and can silently cause syst…

本文如何被排名

报道来源 [1]

ARCS：通过结构化消歧实现精确的文本到SQL

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