I think that using large language models to summarize email (especially marketing), news, social media posts or any type of content that uses a lot of formulaic writing is going to generate lots of errors.
The way I understand large language models, they create chains of words statistically, based on “what is this most likely to say based on my training material”?
In marketing emails, the same boilerplate language is used to say very different things.
“You have been selected” emails have similar wording to “sorry this time you have not won but…”. Same cheery “thanks for being such a wonderful sucker” tone and 99% similar verbiage except for a crucial “NOT” here and there.
I think that using large language models to summarize email (especially marketing), news, social media posts or any type of content that uses a lot of formulaic writing is going to generate lots of errors.
The way I understand large language models, they create chains of words statistically, based on “what is this most likely to say based on my training material”?
In marketing emails, the same boilerplate language is used to say very different things. “You have been selected” emails have similar wording to “sorry this time you have not won but…”. Same cheery “thanks for being such a wonderful sucker” tone and 99% similar verbiage except for a crucial “NOT” here and there.