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This work test several proposed ``state-of-the-art''solutions from the literature and find that older methods are currently far superior for general use, and finds that a new and simple baseline, using LBFGS on a sub-gradient, is highly effective with minor tweaks, despite being dismissed in the literature for theoreti...
Whether a judge returns the same verdict when the same request is worded differently remains largely unexamined, so decoding noise is not charged to wording, and the release lets a reader ask the same of any judge not in the roster.
This work introduces and empirically validate a design framework for compression-based ML, finding compression-based methods competitive with conventional baselines and decisively stronger on malware.
A dual-metric framework consisting of Abstention Rate (AR) and Hallucination Rate (HR) is proposed to characterize the trade-off between reliability and responsiveness to demonstrate that citation attribution systems should be evaluated not only for correctness but also for their ability to abstain appropriately under...
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