256 citations · 1.5k across the 113 of their papers we have counts for
6 papers · 1 filter
AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable
Meysam Alizadeh, Fabrizio Gilardi, Mohsen Mosleh +1
The deployment of LLM-based agents in scientific analysis raises opposing concerns: that agents may reduce methodological diversity, or that they may amplify the analytic flexibili…
From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment
Ivo Bueno, Babette Bühler, Philipp Stark +5
Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts, yet they typically provide…
MemeScouts@LT-EDI 2026: Asking the Right Questions -- Prompted Weak Supervision for Meme Hate Speech Detection
Ivo Bueno, Lea Hirlimann, Enkelejda Kasneci
Detecting hate speech in memes is challenging due to their multimodal nature and subtle, culturally grounded cues such as sarcasm and context. While recent vision-language models (…
Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring
Kathrin Seßler, Maurice Fürstenberg, Babette Bühler +1
The manual assessment and grading of student writing is a time-consuming yet critical task for teachers. Recent developments in generative AI, such as large language models, offer…
Benchmarking Large Language Models for Math Reasoning Tasks
Kathrin Seßler, Yao Rong, Emek Gözlüklü +1
The use of Large Language Models (LLMs) in mathematical reasoning has become a cornerstone of related research, demonstrating the intelligence of these models and enabling potentia…
Faithful Attention Explainer: Verbalizing Decisions Based on Discriminative Features
Yao Rong, David Scheerer, Enkelejda Kasneci
In recent years, model explanation methods have been designed to interpret model decisions faithfully and intuitively so that users can easily understand them. In this paper, we pr…