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Empty shelves or lost keys? Recall is the bottleneck for parametric factuality

Nitay Calderon and Gal Yona, Research Scientists, Google Research When LLMs get facts wrong, is it because they never learned them or because they can't recall what they’ve already encoded? Our knowledge profiling framework reveals the latter: frontier LLMs encode nearly all facts, yet struggle to recall many of them. Quick links Paper WikiProfile Share Copy link × Factuality is essential for making Large Language Models (LLMs) reliable. When a model answers a factual question incorrectly, is it because the fact was never encoded, or because the fact is encoded but not accessible? Standard accuracy metrics collapse these cases together, even though they suggest very different limitations and very different interventions. Encoding failures call for scaling model size or expanding data coverage, while recall failures might also point to post-training and inference-time methods that help LLMs better utilize what they already encode. In “ Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality ”, we introduce knowledge profiling …

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12 Aug 2026 09:51 UTC