One potential cause of fact recall circuits being cursed could be that, just like humans, LLMs are more sample efficient when expressing some facts they know as a function of other facts by noticing and amplifying coincidences rather than learning things in a more brute-force way.
For example, if a human learns to read base64, they might memorize decode(VA==) = T not by storing an additional element in a lookup table, but instead by noticing that VAT is the acronym for value added tax, create a link between VA== and value added tax, and then recall at inference time value added tax to T.
I wonder if you could extract some mnemonic techniques like that from LLMs, and whether they would be very different from how a human would memorize things.
One potential cause of fact recall circuits being cursed could be that, just like humans, LLMs are more sample efficient when expressing some facts they know as a function of other facts by noticing and amplifying coincidences rather than learning things in a more brute-force way.
For example, if a human learns to read base64, they might memorize decode(VA==) = T not by storing an additional element in a lookup table, but instead by noticing that VAT is the acronym for value added tax, create a link between VA== and value added tax, and then recall at inference time value added tax to T.
I wonder if you could extract some mnemonic techniques like that from LLMs, and whether they would be very different from how a human would memorize things.