ArgBench unifies 33 existing datasets into a standardized benchmark for testing LLMs across 46 argumentation tasks and analyzes the impact of prompting techniques and model factors on performance.
Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation , booktitle =
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Reinforcement learning with a multi-part reward teaches LLMs to output independent, meaning-preserving sentence edits that raise argument appropriateness close to full rewriting.
Analyses of labeled social media sentences and interpretations show 30% divergence in ethos and pathos, greater variability for charged content, and predictive power for audience attitudes toward the author.
citing papers explorer
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ArgBench: Benchmarking LLMs on Computational Argumentation Tasks
ArgBench unifies 33 existing datasets into a standardized benchmark for testing LLMs across 46 argumentation tasks and analyzes the impact of prompting techniques and model factors on performance.
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Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning
Reinforcement learning with a multi-part reward teaches LLMs to output independent, meaning-preserving sentence edits that raise argument appropriateness close to full rewriting.
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How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messages
Analyses of labeled social media sentences and interpretations show 30% divergence in ethos and pathos, greater variability for charged content, and predictive power for audience attitudes toward the author.