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模型规模的改变主要体现在训练计算量，模型参数量和训练数据集的大小<ref name=":1">Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint  arXiv:2001.08361, 2020. URL <nowiki>https://arxiv.org/abs/2001.08361</nowiki>.</ref><ref name=":2">Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal large language models. NeurIPS, 2022. URL <nowiki>https://arxiv.org/abs/2203.15556</nowiki>.</ref>这三个因素上。研究者通常会分析模型的能力随训练计算量或模型参数量的增加而变化的曲线，其中每个模型的训练计算量是以 FLOPs (Floating Point Operations, 浮点运算数) 来衡量的<ref name=":2" />。使用模型参数量作为x轴通常会与使用 FLOPs 作为x轴会产生形状相似的曲线，这是因为大多数密集Transformer语言模型家族的训练计算量大致与模型参数的数量成比例地变大<ref name=":1" />。研究者通常不会绘制模型能力针对训练集大小的曲线，因为研究者通常在训练模型时使用固定大小的训练集<ref name=":3">Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. URL <nowiki>https://papers.nips.cc/paper/2020/hash/</nowiki> 1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html.</ref><ref name=":4">Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. Scaling language models: Methods, analysis & insights from training Gopher. arXiv preprint arXiv:2112.11446, 2021. URL <nowiki>https://arxiv.org/abs/</nowiki> 2112.11446.</ref>。

模型规模的改变主要体现在训练计算量，模型参数量和训练数据集的大小<ref name=":1">Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint  arXiv:2001.08361, 2020. URL <nowiki>https://arxiv.org/abs/2001.08361</nowiki>.</ref><ref name=":2">Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal large language models. NeurIPS, 2022. URL <nowiki>https://arxiv.org/abs/2203.15556</nowiki>.</ref>这三个因素上。研究者通常会分析模型的能力随训练计算量或模型参数量的增加而变化的曲线，而不会绘制针对训练集大小的曲线。通常，每个模型的训练计算量是以 FLOPs (Floating Point Operations, 浮点运算数) 来衡量的<ref name=":2" />。模型能力关于参数量与模型能力关于 FLOPs 有形状相似的曲线，这是因为大多数密集Transformer语言模型家族的训练计算量大致与模型参数量成比例<ref name=":1" />。研究者通常在训练模型时使用固定大小的训练集<ref name=":3">Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. URL <nowiki>https://papers.nips.cc/paper/2020/hash/</nowiki> 1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html.</ref><ref name=":4">Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. Scaling language models: Methods, analysis & insights from training Gopher. arXiv preprint arXiv:2112.11446, 2021. URL <nowiki>https://arxiv.org/abs/</nowiki> 2112.11446.</ref>，所以因此较难见到模型能力随训练数据集大小而变化的报告。