Frontier models can recover up to 65% of facts they can't directly

Summary

When large language models (LLMs) hallucinate, developers typically assume the model lacks the required facts. Engineering teams diagnose the error as missing knowledge. The standard response is to increase model size, expand training data, or build complex retrieval architectures. A new study by researchers at Google Research and Technion demonstrates that the knowledge is often not missing.