Benchmark plants 100 errors in psychology papers to test AI review
The best system found 71 errors and the pooled systems found 93; seven missed errors were all omissions.
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The best system found 71 errors and the pooled systems found 93; seven missed errors were all omissions.
Logit averaging in ensembles reduces late-training overfitting and delays optimal stopping in fixed-data pretraining.
Theoretical work derives a general framework quantifying stability guarantees for averaging-based ensembles under arbitrary data perturbations via covariance operator norms.
Researchers propose Conformal Relevance, which builds conformal score functions via in-context example curation and ensembling to improve conciseness across seven NLP tasks.
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