mirror of
https://github.com/jackyzha0/quartz.git
synced 2025-12-20 11:24:05 -06:00
8 lines
1.3 KiB
Markdown
8 lines
1.3 KiB
Markdown
The machine learning industry has traditionally adopted an academic approach, focusing primarily on performance across a range of tasks. LLMs like GPT-4 are a testament to this, having been scaled up to demonstrate impressive & diverse task capability. This scaling has also led to [[Theory of Mind Is All You Need|emergent abilities]], debates about the true nature of which rage on.
|
||
|
||
However, general capability doesn't necessarily translate to completing tasks as an individual user would prefer. This is a failure mode that anyone building agents will inevitably encounter. The focus, therefore, needs to shift from how language models perform tasks in a general sense to how they perform tasks on a user-specific basis.
|
||
|
||
Take summarization. It’s a popular machine learning task at which models have become quite proficient...at least from a benchmark perspective. However, when models summarize for users with a pulse, they fall short. The reason is simple: the models don’t know this individual. The key takeaways for a specific user differ dramatically from the takeaways _any possible_ internet user _would probably_ note.
|
||
|
||
So a shift in focus toward user-specific task performance would provide a much more dynamic & realistic approach. Catering to individual needs & paving the way for more personalized & effective ML applications.
|