Unverified50% confidenceBenchmarkExact time
The zero-shot span-based GLiNER model almost completely fails on entity types that depend on institutional function, achieving a micro F1 of only 0.243.
1
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50%
Confidence
Long-term
Relevance
9/23/2026
First Seen
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Related Claims
UnverifiedAfter rebuilding the configuration under the same public identifier on the same day, the model-minus-uniform endpoint value decreased by 0.0429 (H-minus-R comparison 95% CI: [+0.0182, +0.0667]), and with six configuration components changing simultaneously it was impossible to isolate which component caused the difference.65% similarUnverifiedFine-tuned DistilBERT achieves F1=0.9993 on in-distribution testing, but loses 12.9 mean average precision points under topic shift, a greater degradation than the linear model's 5.2 points.65% similarUnverifiedThe trained model achieves a per-token negative log-likelihood (NLL) of 0.93, compared to 12.60 for a randomly initialized twin model of the same architecture.64% similarUnverified20 samples are insufficient to support a reliable multi-class model, and forcing subdivision will introduce noise and overfitting.61% similarUnverifiedCopy generated by GLM-5.2, even when requested to not sound like AI, was detected as 100% AI-generated by GPT-Zero61% similar
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