AI說書 - 從0開始 - 223 | 第七章額外閱讀項目

我想要一天分享一點「LLM從底層堆疊的技術」，並且每篇文章長度控制在三分鐘以內，讓大家不會壓力太大，但是又能夠每天成長一點。

從 AI說書 - 從0開始 - 193 | 第七章引言 到 AI說書 - 從0開始 - 222 | GPT 4 & RAG 測試，我們完成書籍：Transformers for Natural Language Processing and Computer Vision, Denis Rothman, 2024 第七章說明。

以下附上參考項目：

• OpenAI and GPT-3 engines: https://beta.openai.com/docs/engines/engines
• BertViz GitHub Repository by Jesse Vig: https://github.com/jessevig/bertviz
• OpenAI’s supercomputer: https://blogs.microsoft.com/ai/openai-azure-supercomputer/
• Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, 2018, Improving Language Un-derstanding by Generative Pre-Training: https://cdn.openai.com/research-covers/language- unsupervised/language_understanding_paper.pdf
• Alec Radford et al., 2019, Language Models are Unsupervised Multi-task Learners: https://cdn. openai.com/better-language-models/language_models_are_unsupervised_multitask_ learners.pdf
• Common Crawl data: https://commoncrawl.org/overview/
• GPT-4 Technical Report, OpenAI 2023: https://arxiv.org/pdf/2303.08774.pdf

以下附上額外閱讀項目：

• Alex Wang et.al, 2019, GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding: https://arxiv.org/pdf/1804.07461.pdf
• Alex Wang et al., 20192, SuperGLUE: A Stickier Benchmark for General-Purpose Language Under- standing Systems: https://w4ngatang.github.io/static/papers/superglue.pdf
• Tom B. Brown et al., 2020, Language Models are Few-Shot Learners: https://arxiv.org/ abs/2005.14165
• Chi Wang et al., 2023, Cost-Effective Hyperparameter Optimization for Large Language Model Gen- eration Inference: https://arxiv.org/abs/2303.04673
• Vaswani et al., 2017, Attention Is All You Need: https://arxiv.org/abs/1706.03762