Recognizing the imperative to address the reliability and transparency issues
of Large Language Models (LLM), this work proposes an LLM maturity model
tailored for text-to-query applications. This maturity model seeks to fill the
existing void in evaluating LLMs in such applications by incorporating
dimensions beyond mere correctness or accuracy. Moreover, this work introduces
a real-world use case from the law enforcement domain and showcases QueryIQ, an
LLM-powered, domain-specific text-to-query assistant to expedite user workflows
and reveal hidden relationship in data.

基于大型语言模型（LLM）的可靠性和透明度问题，在法執執法執執执執執警域域中介绍了一个针对文本转查询应用的 LLM 成熟度模型。通过加入除了正确性或准确性以外的维度，该成熟度模型旨在弥补对 LLM 在此类应用中的评估的不足。此外，还提出了一个真实世界的用例，并展示了 QueryIQ，一个由 LLM 驱动的、领域特定的文本转查询助手，以加快用户工作流程并揭示数据中的隐藏关系。

一种用于可靠和透明的文本到查询的 LLM 成熟度模型

An LLM Maturity Model for Reliable and Transparent Text-to-Query

Assessing the maturity of security practices during the development of
Machine Learning (ML) based software components has not gotten as much
attention as traditional software development. In this Blue Sky idea paper, we
propose an initial Machine Learning Security Maturity Model (MLSMM) which
organizes security practices along the ML-development lifecycle and, for each,
establishes three levels of maturity. We envision MLSMM as a step towards
closer collaboration between industry and academia.

提出了一个基于机器学习的软件组件开发安全成熟度模型，将安全实践沿着机器学习开发生命周期组织起来，并为每个实践建立了三个成熟度水平以促进产业与学术界之间更紧密的合作。