  How-to procedures, such as how to plant a garden, are now used by millions of
users, but sometimes need customizing to meet a user's specific needs, e.g.,
planting a garden without pesticides. Our goal is to measure and improve an
LLM's ability to perform such customization. Our approach is to test several
simple multi-LLM-agent architectures for customization, as well as an
end-to-end LLM, using a new evaluation set, called CustomPlans, of over 200
WikiHow procedures each with a customization need. We find that a simple
architecture with two LLM agents used sequentially performs best, one that
edits a generic how-to procedure and one that verifies its executability,
significantly outperforming (10.5% absolute) an end-to-end prompted LLM. This
suggests that LLMs can be configured reasonably effectively for procedure
customization. This also suggests that multi-agent editing architectures may be
worth exploring further for other customization applications (e.g. coding,
creative writing) in the future.
