AI說書 - 從0開始 - 97

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 - 73 到 AI說書 - 從0開始 - 96，我們完成書籍：Transformers for Natural Language Processing and Computer Vision, Denis Rothman, 2024 第三章說明。

以下附上參考項目：

• Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman, 2019, SuperGLUE: A Stickier Benchmark for General-Purpose Lan- guage Understanding Systems: https://w4ngatang.github.io/static/papers/superglue.pdf
• Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman, 2019, GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding: https://arxiv.org/abs/1804.07461
• Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang, 2019, ERNIE 2.0: A Continual Pretraining Framework for Language Understanding: https://arxiv. org/pdf/1907.12412.pdf
• Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S. Gordon, 2011, Choice of Plausible Alterna- tives: An Evaluation of Commonsense Causal Reasoning: https://people.ict.usc.edu/~gordon/ publications/AAAI-SPRING11A.PDF
• Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts, 2013, Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank: https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf
• Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew, 2019, HuggingFace’s Transformers: State-of-the-art Natural Language Processing: https://arxiv.org/abs/1910.03771
• Hugging Face transformer usage: https://huggingface.co/docs/transformers/main/en/ quicktour

以下附上額外閱讀項目：

• Hugging Face is a good place to continue with the Open LLM Leaderboard: https:// huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
• Google provides a comprehensive benchmarking service with BIG-bench and 200+ tasks: https://github.com/google/BIG-bench