Ah, my bad. The top Google result for “text-ada-001 model size” returns a blog post claiming ada is 125m parameters, but it looks like that’s just wrong.
If the training set includes texts of the form “A is B. A is also C”, then you have both orders present (A is B and B is A) and so the Reversal Curse is not applicable.
Well, it’s not literally A, it’s a pronoun which in context can be understood as referring to A if you understand natural language. Do you think the effect goes away if you finetune on data of the form Daphne Barrington is / the director of "A Journey Through Time". She (cutting off the answer as early as “She”)?
Anyway, I still think the reversal curse is more about a deficiency in the training process rather than the model itself; even weak models are clearly capable of doing logical deduction given the right setup (e.g. within a prompt), so the question is more like, how good does the training process have to be (and maybe how big does the model have to be) for the model to be reliably capable of doing logical deduction on:
facts that are present in its prompt (pretty easy)
facts that are present in the finetuning data (pretty hard, apparently)
facts that are in the pretraining data (maybe in-between, and maybe also depends on the specifics of the pretraining process?)
e.g. What happens if you train on the word-wise reversal of all your data? Literally add {The word-wise reversal of the previous text is: ' '.join(reversed(training_doc.split(' ')))} to all your pretraining data, and then train the model on the (twice as large, very redundant) dataset.
Even if something simple like that doesn’t actually make the reversal curse go away, I expect that there is some training process, not too much more sophisticated that current pretraining processes, which does work when applied to current models, or at least to current model architectures (perhaps scaled up a bit).
Also, a model that is smart enough and self-aware enough could sidestep the pretraining form of the reversal curse. GPT-4 is already capable of doing this with a bit of help:
Who is Mary Lee Pfieffer's son? If you don't know, list out some famous celebrities and their mothers' names to see if you can discover the answer within yourself.
Usually causes GPT-4 to get the right answer pretty quickly.
Ah, my bad. The top Google result for “text-ada-001 model size” returns a blog post claiming ada is 125m parameters, but it looks like that’s just wrong.
Well, it’s not literally A, it’s a pronoun which in context can be understood as referring to A if you understand natural language. Do you think the effect goes away if you finetune on data of the form
Daphne Barrington is / the director of "A Journey Through Time". She(cutting off the answer as early as “She”)?Anyway, I still think the reversal curse is more about a deficiency in the training process rather than the model itself; even weak models are clearly capable of doing logical deduction given the right setup (e.g. within a prompt), so the question is more like, how good does the training process have to be (and maybe how big does the model have to be) for the model to be reliably capable of doing logical deduction on:
facts that are present in its prompt (pretty easy)
facts that are present in the finetuning data (pretty hard, apparently)
facts that are in the pretraining data (maybe in-between, and maybe also depends on the specifics of the pretraining process?)
e.g. What happens if you train on the word-wise reversal of all your data? Literally add
{The word-wise reversal of the previous text is: ' '.join(reversed(training_doc.split(' ')))}to all your pretraining data, and then train the model on the (twice as large, very redundant) dataset.Even if something simple like that doesn’t actually make the reversal curse go away, I expect that there is some training process, not too much more sophisticated that current pretraining processes, which does work when applied to current models, or at least to current model architectures (perhaps scaled up a bit).
Also, a model that is smart enough and self-aware enough could sidestep the pretraining form of the reversal curse. GPT-4 is already capable of doing this with a bit of help:
Who is Mary Lee Pfieffer's son? If you don't know, list out some famous celebrities and their mothers' names to see if you can discover the answer within yourself.Usually causes GPT-4 to get the right answer pretty quickly.
https://chat.openai.com/share/a0af0a58-5ec3-408b-86a7-7a9aa82d3c9d
https://chat.openai.com/share/145cd3e7-2a91-4c6c-8831-f3f2935316ee
A more capable model could probably learn to do this itself, without the “famous celebrities” hint from the user.