Anyone understand how this could work? My mental model for llm is predictive text but here how can it understand cell A1 which has a string is the “header” for all values under it? How does it learn to understand table data like that?
Anyone understand how this could work? My mental model for llm is predictive text but here how can it understand cell A1 which has a string is the “header” for all values under it? How does it learn to understand table data like that?
LLMs already understand table data. "Predictive text" is somewhat true but so reductive that it leads to that kind of misconception.
HN is going to mangle this but here's a quick table:
| Type of Horse | Average Height | Typical Color | |----------------|----------------|-----------------| | Arabian | 15 hh | Bay, Gray | | Thoroughbred | 16 hh | Chestnut, Bay | | Clydesdale | 17.5 hh | Bay with White | | Shetland Pony | 10.5 hh | Black, Chestnut |
And after a prompt "pivot the table so rows are colors":
| Typical Color | Type of Horse | Average Height | |----------------|----------------------------------------|-----------------------| | Bay | Arabian, Thoroughbred, Clydesdale | 15 hh, 16 hh, 17.5 hh | | Gray | Arabian | 15 hh | | Chestnut | Thoroughbred, Shetland Pony | 16 hh, 10.5 hh | | Bay with White | Clydesdale | 17.5 hh | | Black | Shetland Pony | 10.5 hh |
> Anyone understand how this could work? My mental model for llm is predictive text but here how can it understand cell A1 which has a string is the “header” for all values under it? How does it learn to understand table data like that?
I imagine it uses the new Agent Skills features
https://www.anthropic.com/news/skills