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Vera Neplenbroek

ACL 2026×2社会与人本对齐与偏好可解释性强化学习与后训练
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #2/4ACL 2026Poster· Natural Language Processing

One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization

Franziska Weeber · Vera Neplenbroek · Jan Batzner · Sebastian Padó

摘要

Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfair outcomes across groups. Prior work has employed so-called personas, sociodemographic user attributes conveyed to a model, to study bias in LLMs by relying on a...

对齐与偏好社会与人本OpenReview 项目页
作者位次 #2/4ACL 2026Poster· ACL Industry Track

Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider

Adrian Sauter · Vera Neplenbroek · Georgios Vlassopoulos · Gianluigi Bardelloni

摘要

In subscription-based businesses, understanding why a customer intends to churn is as vital as the classification itself. We present a casestudy at a large European telecommunications provider, where we implement Text Bottleneck Models (TBMs) for post-call churn classifica-tion. The TBM distills...

可解释性强化学习与后训练社会与人本OpenReview 项目页

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