用语言特征建模金融专家提问的专业性，可精准区分真专家与模型生成问题。
Modeling Professionalism in Expert Questioning through Linguistic Differentiation
- 基于话语调节、前导语等语言特征构建专业性标注框架。
- 同一语言特征同时关联人工评价与作者来源，相关性显著。
- 仅靠可解释的语言特征就能超越Gemini-2.0等基线模型。
专业性是专家沟通中关键但研究不足的维度，尤其在金融等高风险领域。本文探讨如何利用语言特征建模与评估专家提问中的专业性。我们提出一种新的标注框架，量化金融分析师提问中的结构与语用要素，如话语调节词、前导语和请求类型。基于人工撰写和大语言模型生成的问题，构建两个数据集：一个标注了感知专业性，另一个标记了问题来源。结果表明，相同语言特征与人工判断及作者来源均呈强相关，暗示共同的风格基础。此外，仅使用这些可解释特征训练的分类器，在区分专家撰写的提问上优于Gemini-2.0和SVM基线。研究证明，专业性是一种可学习的、跨领域的可建模特征，可通过语言学基础方法捕捉。
原文摘要 · Abstract (English)
Professionalism is a crucial yet underexplored dimension of expert communication, particularly in high-stakes domains like finance. This paper investigates how linguistic features can be leveraged to model and evaluate professionalism in expert questioning. We introduce a novel annotation framework to quantify structural and pragmatic elements in financial analyst questions, such as discourse regulators, prefaces, and request types. Using both human-authored and large language model (LLM)-generated questions, we construct two datasets: one annotated for perceived professionalism and one labeled by question origin. We show that the same linguistic features correlate strongly with both human judgments and authorship origin, suggesting a shared stylistic foundation. Furthermore, a classifier trained solely on these interpretable features outperforms gemini-2.0 and SVM baselines in distinguishing expert-authored questions. Our findings demonstrate that professionalism is a learnable, domain-general construct that can be captured through linguistically grounded modeling.
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