Fallacy Benchmarks Measure Scheme Recognition, Not Fallacy Detection
Fallacy-detection benchmarks pair fallacy classes with a single "valid" or "none" class that takes everything data collection did not label as a fallacy. A detector has two jobs, deciding whether an argument is fallacious and naming which fallacy it commits, and the false-positive rate is meant to measure the first. We show that what these benchmarks actually score is scheme recognition, the ability behind the second job. Their own test sets already show it: when a classifier misses a fallacy, the error lands on "none" rather than on another fallacy type, so detection is failing while classification holds. The reason is what the valid class lacks. The negatives that separate the two jobs ...
| Authors | Navyansh Singh, Animesh Pathak, Aarav Singh |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 20 September 2026 |
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