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cs.CL2025
IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance
Paul Röttger, Musashi Hinck, Valentin Hofmann +4
Large language models (LLMs) are helping millions of users write texts about diverse issues, and in doing so expose users to different ideas and perspectives. This creates concerns…
cs.CL2024
Steering Large Language Models to Evaluate and Amplify Creativity
Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck +2
Although capable of generating creative text, Large Language Models (LLMs) are poor judges of what constitutes "creativity". In this work, we show that we can leverage this knowled…
cs.CL2024
AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments
Till Raphael Saenger, Musashi Hinck, Justin Grimmer +1
We introduce AutoPersuade, a three-part framework for constructing persuasive messages. First, we curate a large dataset of arguments with human evaluations. Next, we develop a nov…