EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes an A/B testing estimator that introduces a hypothetical middle algorithm for stepwise estimation to induce positive correlation, reducing selection errors and halving required data volume.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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A More Accurate Algorithm Comparison through A/B Testing using Offline Evaluation Methods
Proposes an A/B testing estimator that introduces a hypothetical middle algorithm for stepwise estimation to induce positive correlation, reducing selection errors and halving required data volume.