AI language models mean meaningful pairs of words to truly know what language means and figure out how the vectors of their response contribute to the logic of where you can’t have both if they go in opposite directions. So having sort of a thesaurus for the AI for language models seems important as well as sort of human match paired test. These sentiments are opposite. This is not the thing that language models can really figure out on their own just by statistical analysis. It’s sort of like foreigners learning idioms. Well, language models need to learn idioms to really figure out what logic is. Clean this up, but preserving the intent and... > [!ai]+ AI > > AI language models need more than statistical analysis to truly understand language. They need to grasp the meaning of word pairs and identify relationships like antonyms, where words have opposite meanings. This requires a deeper understanding than simply recognizing co-occurrence. A thesaurus-like resource and human-validated tests for semantic relationships (like identifying antonyms or confirming sentiments are opposite) are crucial for development. Just as foreigners struggle with idioms, language models need to learn these nuanced expressions and relationships to truly grasp logic and meaning.