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.
Logical Fallacy Detection , booktitle =
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
years
2026 8verdicts
UNVERDICTED 8representative citing papers
Audit finds 36-39% incorrect FOL labels in FOLIO and MALLS; corrections raise LLM accuracy 9-22 points and an LLM-guided review framework achieves 90% dataset quality after checking fewer than 24% of examples.
MEDS is a dataset of 28,000 LLM personas performing high-school math tasks alongside psychometric tests and cognitive networks that capture math anxiety, self-efficacy, and confidence to support safer AI tutors.
Reinforcement learning with a multi-part reward teaches LLMs to output independent, meaning-preserving sentence edits that raise argument appropriateness close to full rewriting.
ForEx translates LLM explanations on logical fallacies into Lean4 for formal verification, finding over 90% pass verification but only 20% human label agreement on LOGIC-Climate, exposing a gap invisible to standard metrics.
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.
LLM-extracted patterns merging logical structures and linguistic cues yield statistically significant gains in fallacy classification over zero-shot baselines with cross-dataset generalization.
Lightweight CNN with CBAM, inspection-scene augmentation, and Focal Loss reaches 825 FPS and +2.51% F1 on SDNET2018 bridge crack data.
citing papers explorer
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Math Education Digital Shadows for facilitating learning with LLMs: Math performance, anxiety and confidence in simulated students and AIs
MEDS is a dataset of 28,000 LLM personas performing high-school math tasks alongside psychometric tests and cognitive networks that capture math anxiety, self-efficacy, and confidence to support safer AI tutors.
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ForEx: A Formal Verification Framework for Explainable Reasoning in Logical Fallacy Detection and Annotation
ForEx translates LLM explanations on logical fallacies into Lean4 for formal verification, finding over 90% pass verification but only 20% human label agreement on LOGIC-Climate, exposing a gap invisible to standard metrics.