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arXiv Computation and Language By Daniela Gottesman, Alon Gilae-Dotan, Ido Cohen, Yoav Gur-Arieh, Marius Mosbach, Ori Yoran, Mor Geva

LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations

Read the original on arXiv Computation and Language →

LMEnt is a toolkit designed to study how language models acquire and represent world knowledge. It includes a richly annotated pretraining corpus, an improved entity-based retrieval method, and twelve pretrained models with numerous checkpoints. The suite enables controlled experiments linking entity mentions in training data to downstream knowledge performance, revealing how co-occurrence patterns influence learning and editing difficulty.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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