It's regularly the case that they are simply too long to fit, so editorializing is necessary. Either cutting them slightly short, removing descriptors, excess verbs etc, is better than chopping a word in half.
The other day I wanted to gather Reddit comments about a solar panel vendor. Claude doesn't have access to I had Gemini do some "deep research". When I fed the verbose report back to Claude it basically said it was a bunch of "hallucinated bullshit".
I've never had a Gemini Deep Research report that didn't sound like a load of pseudo-intellectual BS. It always starts with a long grandiose preamble and then sounds way too academic, almost like a caricature of academia.
Isn't it just learning to map specific words, from the "knife world", to the correct class? If so, a simple dictionary would fit.
What I think is a better way to validate is to split train/validation by words used presented in NER classes (like, it should be able to find new brands never seen before). It is a interesting problem.
I found it much more useful to go to a knife shop and handle a whole bunch of knives for myself. They’re all pretty similar besides material, so not much signal you’re going to be able to glean from people arguing on reddit.
Most of the attributes don’t matter. Most people would be much better off with a $50 Victorinox that they kept sharp and a wood cutting board they maintained than upgrading the knife. If you are using it all day there are definitely looking things from a comfort perspective but for most homes, does not matter.
And this can be said to be misleading in my opinion
If he wanted a Gemini replacement verbatim, its called locally inferring it's sibling, Gemma.
Okay!