Clarify Misconceptions about Large Language Models Text Generation

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😎 help me better explain this, in similar words, fixing any major misconceptions or inaccuracies about LLM’s in my text: “In very simplistic terms, what Large Language Models do well is “putting words together” by massively analyzing strings of text and predicting which words come better next to each other to produce meaningful and valuable text.”

🤖 Here are some possible options:

• To put it simply, Large Language Models are good at “creating sentences” by analyzing huge amounts of text and predicting which words go well together to produce meaningful and valuable text [speakai.co]

• In simple terms, Large Language Models excel at “writing text” by learning from massive amounts of text and predicting which words fit well together to produce meaningful and valuable text [en.wikipedia.org]

• Simply put, Large Language Models are skilled at “generating text” by studying enormous amounts of text and predicting which words match well together to produce meaningful and valuable text [www.nytimes.com] [speakai.co] [en.wikipedia.org] [www.nytimes.com]

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Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: