there may be personas which are not present in the training data but which are implied by it
I like this idea—wonder if we can test ‘implied personas’ in some way. A somewhat contrived example below:
Consider a hypothetical movie series with a titular main character that starts out naive and innocent
Over the course of movies 1, 2, 3 we observe that character becoming older, more street-wise, cynical, etc… and let’s suppose that this trend is linear.
Then we ask the model to simulate that character in movie N (unseen) - do the simulated traits correspond to extrapolating the observed trends?
Note: The above example involves ‘extrapolating’ the evolution of a persona over time. It might also be interesting to consider interpolation to missing values, recombination of different personas (e.g. if A and B had children what would that look like?), etc
Another big class of implied personas is the authors of all the text that they’ve encountered.
That also includes the ‘authors’ of texts that don’t actually have an author per se. Some cases I can imagine are
Text from chat platforms where the usernames have been dropped and so it looks like text from one person but is really from many
The ‘authors’ of fictional books that don’t exist
Machine translations which would likely imply authors who are different from the authors of the original translated text
Etc etc.
In some ways this seems like the most central case, since correctly modeling authors is at the heart of the pre-training loss function.
Modeling authors could be seen as a separate category from personas, but I expect that under the hood it’s mostly the same thing using the same mechanisms; a persona is just a particular kind of author (or perhaps vice versa).
I like this idea—wonder if we can test ‘implied personas’ in some way. A somewhat contrived example below:
Consider a hypothetical movie series with a titular main character that starts out naive and innocent
Over the course of movies 1, 2, 3 we observe that character becoming older, more street-wise, cynical, etc… and let’s suppose that this trend is linear.
Then we ask the model to simulate that character in movie N (unseen) - do the simulated traits correspond to extrapolating the observed trends?
Note: The above example involves ‘extrapolating’ the evolution of a persona over time. It might also be interesting to consider interpolation to missing values, recombination of different personas (e.g. if A and B had children what would that look like?), etc
Another big class of implied personas is the authors of all the text that they’ve encountered.
That also includes the ‘authors’ of texts that don’t actually have an author per se. Some cases I can imagine are
Text from chat platforms where the usernames have been dropped and so it looks like text from one person but is really from many
The ‘authors’ of fictional books that don’t exist
Machine translations which would likely imply authors who are different from the authors of the original translated text
Etc etc.
In some ways this seems like the most central case, since correctly modeling authors is at the heart of the pre-training loss function.
Modeling authors could be seen as a separate category from personas, but I expect that under the hood it’s mostly the same thing using the same mechanisms; a persona is just a particular kind of author (or perhaps vice versa).