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Henning Wachsmuth

ACL 2026×2NeurIPS 2026社会与人本评测与基准强化学习与后训练推理多模态
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0 一作 · 3 篇 · ACL 2026 / NeurIPS 2026

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2026 顶会论文

3
作者位次 #3/3NeurIPS 2026Poster

Too Early for AI-Assisted Peer Review: A Systematic Account of the Limits and Opportunities of Automating Human Judgment

Annette Hautli-Janisz · Elena Musi · Henning Wachsmuth

社会与人本OpenReview
作者位次 #3/3ACL 2026Poster· Natural Language Processing

Teaching LLM s Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning

Timon Ziegenbein · Maja Stahl · Henning Wachsmuth

摘要

Editing human-written text has become a standard use case of large language models (LLMs), for example, to make one’s arguments more appropriate for a discussion. Comparing human to LLM-generated edits, however, we observe a mismatch in editing strategies: While LLMs often perform multiple scatte...

强化学习与后训练评测与基准社会与人本OpenReview 项目页
作者位次 #4/4ACL 2026Findings· Findings of ACL

A rg B ench: Benchmarking LLM s on Computational Argumentation Tasks

Yamen Ajjour · Carlotta Quensel · Nedim Lipka · Henning Wachsmuth

摘要

Argumentation skills are an essential toolkit for large language models (LLMs). These skills are crucial in various use cases, including self-reflection, debating collaboratively for diverse answers, and countering hate speech. In this paper, we create the first benchmark for a standardized evalu...

评测与基准推理多模态OpenReview 项目页

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