activity
20242026
collaborators

12 papers

cs.CL2026

Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models

Georg Ahnert, Anna-Carolina Haensch, Barbara Plank +1

Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LLMs are typically trained to gene…

cs.CL2026

Capabilities and Evaluation Biases of Large Language Models in Classical Chinese Poetry Generation: A Case Study on Tang Poetry

Bolei Ma, Yina Yao, Anna-Carolina Haensch

Large Language Models (LLMs) are increasingly applied to creative domains, yet their performance in classical Chinese poetry generation and evaluation remains poorly understood. We…

cs.CL2026

Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation

Bolei Ma, Yong Cao, Indira Sen +4

Large Language Models (LLMs) are increasingly used to simulate public opinion and other social phenomena. Most current studies constrain these simulations to multiple-choice or sho…

stat.OT2025

Beyond Correctness: Evaluating and Improving LLM Feedback in Statistical Education

Niklas Ippisch, Markus Herklotz, Anna-Carolina Haensch +1

Large language models (LLMs) have been proposed as scalable tools to address the gap between the importance of individualized written feedback and the practical challenges of provi…

stat.OT2025

Can we trust LLMs as a tutor for our students? Evaluating the Quality of LLM-generated Feedback in Statistics Exams

Markus Herklotz, Niklas Ippisch, Anna-Carolina Haensch

One of the central challenges for instructors is offering meaningful individual feedback, especially in large courses. Faced with limited time and resources, educators are often fo…

cs.CL2025

Systematic Evaluation of Uncertainty Estimation Methods in Large Language Models

Christian Hobelsberger, Theresa Winner, Andreas Nawroth +2

Large language models (LLMs) produce outputs with varying levels of uncertainty, and, just as often, varying levels of correctness; making their practical reliability far from guar…