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arXiv Machine Learning By Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov, Volker Tresp

A Storage-Retrieval Gap in Parametric Knowledge Graph Memory

Read the original on arXiv Machine Learning →

The paper investigates a parametric approach to knowledge graph memory by compiling each entity into a LoRA adapter, enabling zero‑cost query-time retrieval via weight injection. On the MetaQA dataset, these adapters encode context‑free factual knowledge, improving exact‑match scores by up to +0.243 over a base model and achieving an oracle gap of +0.283. However, the stored knowledge is not recoverable through similarity or embedding‑based methods, indicating that knowledge is stored locally and does not transfer across semantically neighboring entities.

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 Machine Learning.

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