A new stratified per-question probability-gap metric shows that existing contamination mitigation strategies overstate restoration, and a step-wise decoding cap (RailCap) achieves the lowest measured gap.
Unveiling the spectrum of data contamination in language model: A survey from detection to remediation
5 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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A new paired-prompt protocol reveals alignment-pipeline-specific heterogeneity in how open-weight LLMs respond to evaluation versus deployment framings.
MISS-QA, a new benchmark for information-seeking QA over schematic diagrams, shows the best open-source multimodal model at 61.6% accuracy versus 89.0% for human experts.
InSight is a 21,349-claim benchmark that evaluates vision-language agents on verifying claims against interactive Vega-Lite visualizations and scores the efficiency of their interaction traces.
Introduces a parameter-driven framework for data attribution in LLMs that enables negotiation among creators, users, and intermediaries to meet stakeholder goals within the data economy.
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A Human-Centric Framework for Data Attribution in Large Language Models
Introduces a parameter-driven framework for data attribution in LLMs that enables negotiation among creators, users, and intermediaries to meet stakeholder goals within the data economy.