Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
Towards a Science of AI Evaluations,
2 Pith papers cite this work. Polarity classification is still indexing.
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Open-weight AI models mostly fail four proposed proportional evaluation criteria (PE1-4) designed to address risks from public weights that closed models do not face.
citing papers explorer
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Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior
Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
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Open Weight AI Models Require Proportional Evaluation Approaches
Open-weight AI models mostly fail four proposed proportional evaluation criteria (PE1-4) designed to address risks from public weights that closed models do not face.