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cset.georgetown.edu | ||
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jalammar.github.io
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| | | | | Discussion: Discussion Thread for comments, corrections, or any feedback. Translations: Korean, Russian Summary: The latest batch of language models can be much smaller yet achieve GPT-3 like performance by being able to query a database or search the web for information. A key indication is that building larger and larger models is not the only way to improve performance. Video The last few years saw the rise of Large Language Models (LLMs) - machine learning models that rapidly improve how machines process and generate language. Some of the highlights since 2017 include: The original Transformer breaks previous performance records for machine translation. BERT popularizes the pre-training then finetuning process, as well as Transformer-based contextualized... | |
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trishagee.com
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| | | | | I'm working on a new talk which aims to address some of the issues that face developers when it comes to running automated tests. Please take my super-scientific survey so that I can take a look at the real issues facing developers, and structure my talk around them. Thanks! https://www.surveymonkey.com/r/TXSDVH5 Author Trisha Gee Trisha is ... Continue reading "Is testing getting in the way of your productivity?" | |
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www.computerworld.com
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| | | | | Large language models are the algorithmic basis for chatbots like OpenAI's ChatGPT and Google's Bard. The technology is tied back to billions - even trillions - of parameters that can make them both inaccurate and non-specific for vertical industry use. Here's what LLMs are and how they work. | |
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erickerr.com
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