VISTAQA is a new benchmark for joint visual question answering correctness and pixel-level grounding, evaluated with the GROVE metric that uses per-sample geometric mean to require both dimensions to succeed.
A coefficient of agreement for nominal scales.Educational and Psychological Measurement, 20(1):37–46, 1960
3 Pith papers cite this work. Polarity classification is still indexing.
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LFD discovers predictive text features via LLM contrastive proposals, cross-LLM Cohen's kappa screening, and residual held-out gain selection, matching baseline accuracy while achieving higher human agreement and lower label leakage on ten tasks.
Large-scale LLM analysis of 16k CTI reports over 20 years shows a fragmented vendor ecosystem with low overlap and reporting biases.
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VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence
VISTAQA is a new benchmark for joint visual question answering correctness and pixel-level grounding, evaluated with the GROVE metric that uses per-sample geometric mean to require both dimensions to succeed.
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Interpretable Discriminative Text Representations via Agreement and Label Disentanglement
LFD discovers predictive text features via LLM contrastive proposals, cross-LLM Cohen's kappa screening, and residual held-out gain selection, matching baseline accuracy while achieving higher human agreement and lower label leakage on ten tasks.
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The CTI Echo Chamber: Fragmentation, Overlap, and Vendor Specificity in Twenty Years of Cyber Threat Reporting
Large-scale LLM analysis of 16k CTI reports over 20 years shows a fragmented vendor ecosystem with low overlap and reporting biases.