Frontier LLMs know more facts than they can recall
What happened
Researchers at Google have found that large language models (LLMs) have access to a vast amount of knowledge, but struggle to recall specific facts when needed. This is because the models are limited by their ability to process and retrieve information from their vast databases. This bottleneck can lead to 'empty shelves' or 'lost keys' scenarios where the model knows the information exists, but can't retrieve it.
Why it matters
This discovery highlights the need for more efficient and scalable methods for storing and retrieving information in LLMs, which could have significant implications for the development of more accurate and reliable AI systems.
Is this a big deal?
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