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Babette Bühler

AAAI 2026ACL 2026数据与预训练可解释性强化学习与后训练
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1 一作 · 2 篇 · AAAI 2026 / ACL 2026

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2026 顶会论文

2
作者位次 #2/8ACL 2026Findings· Findings of ACL

From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment

Ivo Bueno · Babette Bühler · Philipp Stark · Tim Fütterer · Ulrich Trautwein · Dorottya Demszky · Heather Hill · Enkelejda Kasneci

摘要

Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts, yet they typically provide little insight into why a particular score is produced. We propose a general framework for sentence-level interpretab...

数据与预训练可解释性强化学习与后训练OpenReview 项目页
一作AAAI 2026Poster· Artificial Intelligence

Democratizing Writing Support with AI: Insights from One Year of Real-World Interactions with an Open-Access Writing Feedback Tool

Babette Bühler · Ivo Bueno · Enkelejda Kasneci

常见合作者