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I had exactly the same idea a while back: https://blog.scottlogic.com/2022/02/23/word-embedding-recommendations.html https://blog.scottlogic.com/2022/02/23/wor

I had exactly the same idea a while back: https://blog.scottlogic.com/2022/02/23/word-embedding-recommendations.html https://blog.scottlogic.com/2022/02/23/word-embedding-recomm... Using embeddings I increased engagement with related articles. Personally I think embeddings are a powerful tool that are somewhat overlooked. They can be used to navigate between documents (and excerpts) based on similarities - or conversely find unique content. All without worrying about hallucinations. In other words, they are quite ‘safe’

mike_hearn 3y ago
> All without worrying about hallucinations. In other words, they are quite ‘safe’ Within limits, yes. In some use cases a vector notion of similarity isn't always ideal. For example, in the article "France" and "Germany" are considered similar. Yes, they are, but if you're searching for stuff about France then stuff about Germany is a false positive. Embeddings can also struggle with logical opposites. Hot/cold are in many senses similar concepts, but they are also opposites. Finding the opposite of what you're searching for isn't always helpful. I wouldn't say embeddings are overlooked exactly? Right now it feels like man+dog are building embedding based search engines. The next frontier is probably going to be balancing conventional word based approaches with embeddings to really maximize result quality, as sometimes you want "vibes" and sometimes you want control.