SMALL MODELS, BIG RESULTS: ACHIEVING SUPERIOR INTENT EXTRACTION THROUGH DECOMPOSITION

Summary from the TLDR AI newsletter

For agents to be truly helpful, the underlying models need to be able to understand what users are trying to do when they are interacting with them. Large multimodal LLMs are already good at understanding user intent, but this typically involves sending information to a server, which can be slow, costly, and carries the potential risk of exposing sensitive information. The task can be made more tractable for small models by separating user intent understanding into two stages.