Simon Willison
Doug Turnbull 提出让模型先幻觉出标签再通过嵌入匹配现有词汇的方法，为分类任务提供新思路
AI 摘要
Doug Turnbull 提出让模型先幻觉出标签再通过嵌入匹配现有词汇的方法，为分类任务提供新思路。
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原文
Doug Turnbull has a neat solution. Tell the model to output tags without any details of the existing vocabulary, then use vector embeddings against the existing corpus to find the concrete tags that are closest to the ones the model imagined might fit!
His example prompt suggests including an example of the shape of your tags to help the model make a more useful guess:
Your task is to create novel, never seen before, furniture, home goods, or hardware classification that best fit a search query.Product classifications might look like:Furniture / Living Room Furniture / Coffee Tables&End Tables / Coffee TablesDécor&Pillows / Decorative Pillows&Blankets / Throw PillowsFurniture / Bedroom Furniture / Dressers&ChestsKitchen&Tabletop / Kitchen Organization / Food Storage&CanistersSchool Furniture and Supplies / School Furniture / School Chairs&Seating / Stackable ChairsBaby&Kids / Toddler&Kids Bedroom Furniture / Kids BedsHere's the query to generate classifications for:brown coffee table
Tags: search, ai, generative-ai, llms, embeddings, doug-turnbull
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