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in . The advent of large language models () has revolutionized , enabling the generation of coherent and contextually relevant text. We find that: 1) simulated in the of some LLMs (under specific prompting configurations) is reliable and valid arxiv.org/abs/2307.00184

#personality #traits #largelanguagemodels #llms #naturallanguageprocessing #Outputs

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How to understand and interpret ChatGPT’s output - Uncover how to understand and interpret ChatGPT’s output while st... - cointelegraph.com/news/underst -generatedtext

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So my rough understanding of operation is that there’s a tokenizer, a that was trained on a massive corpus, and a detokenizer.

Could one keep, say, the tokenizer/detokenizer from -3, but train a totally new neural network with a totally new corpus and fitness function? And how would one go about doing that?

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Developers and data scientists are using generative AI and large language models (LLMs) to query unstructured data. Open source LLMs such as Dolly, EleutherAI, Pythia, Meta AI, and LLaMa are popular tools for experimenting with natural language processing (NLP). Organizations are increasing spending on NLP and prioritizing... infoworld.com/article/3701289/

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