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Anna-Carolina Haensch

ACL 2026×2社会与人本推理对齐与偏好评测与基准多模态
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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

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

Georg Ahnert · Anna-Carolina Haensch · Barbara Plank · Markus Strohmaier

摘要

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 generate open-ended text instead. Previous research has used a diverse range of methods for generating closed-ended surve...

社会与人本推理对齐与偏好OpenReview 项目页
作者位次 #3/3ACL 2026Findings· Findings of ACL

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

Bolei Ma · Yina Yao · Anna-Carolina Haensch

摘要

Large Language Models (LLMs) are increasingly applied to creative domains, yet their performance in classical Chinese poetry generation and evaluation remains poorly understood. We propose a three-step evaluation framework that combines computational metrics, LLM-as-a-judge assessment, and human...

社会与人本评测与基准多模态OpenReview 项目页

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