# Modeling Professionalism in Expert Questioning through Linguistic Differentiation

arXiv: https://arxiv.org/abs/2507.20249 · HTML: https://mlsanity.com/paper/2507.20249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [Co-Trained Retriever-Generator Framework for Question Generation in Earnings Calls](https://mlsanity.com/paper/2409.18677/) (2024)

## Abstract (arXiv)

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.