EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
FAST-MEL claims to match top multimodal entity linking accuracy while running three orders of magnitude faster and using one order of magnitude less storage via a novel compact vector representation.
citing papers explorer
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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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Formalized Information Needs Improve Large-Language-Model Relevance Judgments
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
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FAST-MEL: A Fast, Accurate, and Storage Efficient Solution for Multimodal Entity Linking
FAST-MEL claims to match top multimodal entity linking accuracy while running three orders of magnitude faster and using one order of magnitude less storage via a novel compact vector representation.