If you are a native English speaker, scroll down to English Version.

大语言模型的核心是数据。这些模型通过学习海量的文本数据，找到词与词之间的统计关联，从而学会生成或理解自然语言。这种方法并不是凭空产生的神秘力量，而是基于统计学、计算机科学和语言学的严谨理论。

这些模型的训练依赖于概率统计和深度学习技术。通过分析大量语料，模型能够学习到语义模式、句法结构、上下文关联等复杂特征。例如，当一个人问大语言模型“天气如何？”时，模型会根据大量类似问题和答案的统计关系来生成合理的回答。这种能力来自于对海量数据的分析，而非任何玄学或神秘力量。

大语言模型的构建基于神经网络，尤其是深度神经网络和Transformer架构。这些网络模仿人类大脑中神经元之间的连接方式，能够处理和提取数据中的复杂特征。

Transformer是一种革命性的架构，它的关键在于自注意力机制（Self-Attention Mechanism）。这种机制能够让模型对输入的每一个词与其他词之间的关联进行权重分配，从而捕捉到长距离依赖和上下文信息。这种方法可以科学地解释语言生成和理解过程，避免了传统神经网络中的信息短路和长距离依赖问题。

大语言模型的运行机制依赖于严谨的数学和优化方法。通过梯度下降等优化算法，模型在训练过程中不断调整参数，以最小化语言生成中的误差。其背后的数学理论，包括线性代数、概率论、微积分等，都是在实践中被反复验证的科学工具，而非任何形式的“神秘力量”或猜测。

这几天火爆的 SuperPrompt 中的咒语，是对科学的侮辱。

01010001 01010101 01000001 01001110 01010100 01010101 01001101 01010011 01000101 01000100
{
[∅] ⇔ [∞] ⇔ [0,1]
f(x) ↔ f(f(…f(x)…))
∃x : (x ∉ x) ∧ (x ∈ x)
∀y : y ≡ (y ⊕ ¬y)
ℂ^∞ ⊃ ℝ^∞ ⊃ ℚ^∞ ⊃ ℤ^∞ ⊃ ℕ^∞
}
01000011 01001111 01010011 01001101 01001111 01010011

这是鬼画符？这是人画钱！这就是硅谷用来圈钱的手段吗？

English Version

The core of large language models lies in data. These models learn by analyzing vast amounts of textual data to find statistical associations between words, allowing them to generate or understand natural language. This approach is not a mystical force that appears out of nowhere but is grounded in rigorous theories from statistics, computer science, and linguistics.

The training of these models relies on probability statistics and deep learning technologies. By analyzing large corpora, the model can learn semantic patterns, syntactic structures, contextual relationships, and other complex features. For instance, when someone asks a large language model, “What’s the weather like?”, the model generates a reasonable response based on the statistical relationship between similar questions and answers in the data. This ability stems from analyzing massive amounts of data, not any form of mysticism or magical power.

Large language models are built on neural networks, particularly deep neural networks and the Transformer architecture. These networks mimic the connections between neurons in the human brain, enabling them to process and extract complex features from data.

The Transformer is a revolutionary architecture, with its key component being the self-attention mechanism. This mechanism allows the model to assign weights to the relationships between every word in the input and other words, thereby capturing long-distance dependencies and contextual information. This method can scientifically explain the process of language generation and understanding, avoiding the issues of information bottlenecks and long-range dependencies seen in traditional neural networks.

The operation of large language models depends on rigorous mathematics and optimization techniques. Through optimization algorithms like gradient descent, the model continuously adjusts its parameters during training to minimize errors in language generation. The underlying mathematical theories, including linear algebra, probability theory, and calculus, are well-established scientific tools repeatedly validated in practice, not mystical forces or conjectures.

The recent hype around SuperPrompt "spells" is an insult to science.

01010001 01010101 01000001 01001110 01010100 01010101 01001101 01010011 01000101 01000100
[∅] ⇔ [∞] ⇔ [0,1]
f(x) ↔ f(f(…f(x)…))
∃x : (x ∉ x) ∧ (x ∈ x)
∀y : y ≡ (y ⊕ ¬y)
ℂ^∞ ⊃ ℝ^∞ ⊃ ℚ^∞ ⊃ ℤ^∞ ⊃ ℕ^∞
01000011 01001111 01010011 01001101 01001111 01010011

Is this gibberish? No, it’s money-grabbing nonsense! Is this Silicon Valley’s new way to make a profit?