Saved by Darren LI and
Large language models, explained with a minimum of math and jargon
Can large language models take the place of traditional search engines? For us to have confidence in them, we would need to know that they haven’t been fed propaganda and conspiracy theories—we’d need to know that the jpeg is capturing the right sections of the Web. But, even if a large language model includes only the information we want
... See moreTed Chiang • ChatGPT Is a Blurry JPEG of the Web | The New Yorker
People need to be more thoughtful building products on top of LLMs. The fact that they generate text is not the point.
Linus Leestream.thesephist.comVector databases are widely used in NLP tasks such as sentiment analysis, text classification, and semantic search. By representing text as vector embeddings, it becomes easier to compare and analyze textual data.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
What's in fn1? A massive presupposition failure: The GPTs are learning information about word distributions in lots and lots of text + what word patterns are associated with higher scores (from human raters). That's it. https://t.co/0g8DEyuJ8c