研究人员开发了GradeSQL，一个用于提高大型语言模型（LLM）在文本到SQL任务中可靠性的新框架。该框架利用结果奖励模型（ORMs）作为测试时验证的学习语义评分函数。GradeSQL使用自动候选生成和基于执行的标注来训练这些ORMs，无需人工标注。当集成到Best-of-N管道中时，基于ORM的选择在BIRD和Spider基准测试上的性能显著优于传统方法。 AI
影响 增强了LLM在文本到SQL等结构化推理任务中的可靠性和准确性，有望提高复杂查询的性能。
排序理由 该集群包含一篇研究论文，详细介绍了一个用于提高LLM在特定任务上性能的新框架和方法。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.30851v1 Announce Type: cross Abstract: Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL. Common test-time inference strategies, including Best-of-N sampling and Majority…
Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL. Common test-time inference strategies, including Best-of-N sampling and Majority Voting, rely on heuristic signals such as executi…