Prompt Without Words: Distilling GPT Semantics for Smarter Vision Models
TL;DR for operators Most attempts to improve CLIP-style image classification with large language models follow a familiar ritual: ask GPT to describe a class, paste those descriptions into prompts, then hope the model pays attention to the useful bits. The problem is that GPT’s descriptions are not stable objects. They vary by query wording, include hedged statements, and sometimes contain features that are hard or impossible to verify visually. “Usually,” “may,” and “often” are not exactly the foundations of a disciplined recognition system. ...