大型语言模型（LLM）的工作原理是什么？2026年最新技术解析与应用前景

什么是大型语言模型？

大型语言模型，通常简称为 LLM，是基于海量数据进行预训练的超大规模深度学习模型。其核心架构是 Transformer，这是一组由具备自注意力机制的编码器和解码器构成的神经网络。编码器和解码器能够从文本序列中提取语义，并理解单词与短语之间的关联。

Large Language Models, commonly abbreviated as LLMs, are extremely large deep learning models pre-trained on massive datasets. Their core architecture is the Transformer, a set of neural networks composed of encoders and decoders with self-attention mechanisms. Encoders and decoders can extract meaning from text sequences and understand the relationships between words and phrases.

Transformer 模型能够进行无监督训练，更准确地说，它具备自主学习能力。通过这一过程，模型可以学会理解基础的语法、语言和知识。

Transformer models are capable of unsupervised training, or more precisely, they possess self-learning capabilities. Through this process, the model learns to understand fundamental grammar, language, and knowledge.

与早期按顺序处理输入的循环神经网络（RNN）不同，Transformer 并行处理整个输入序列。这使得数据科学家能够利用 GPU 来训练基于 Transformer 的 LLM，从而大幅缩短训练时间。

Unlike earlier Recurrent Neural Networks (RNNs) that process inputs sequentially, Transformers process entire input sequences in parallel. This allows data scientists to leverage GPUs for training Transformer-based LLMs, significantly reducing training time.

得益于 Transformer 神经网络架构，我们可以构建参数规模极其庞大的模型，通常达到数千亿级别。如此大规模的模型能够消化海量数据，这些数据通常来自互联网，包括 Common Crawl（包含超过 5000 亿个网页）和维基百科（约 5700 万个页面）等来源。

Thanks to the Transformer neural network architecture, we can build models with an extremely large scale of parameters, often reaching hundreds of billions. Models of this scale can ingest massive amounts of data, typically sourced from the internet, including sources like Common Crawl (containing over 500 billion web pages) and Wikipedia (approximately 57 million pages).

为什么大型语言模型如此重要？

大型语言模型具有极高的灵活性。一个模型可以执行多种截然不同的任务，例如回答问题、总结文档、翻译语言和补全句子。LLM 有潜力彻底改变内容创作的方式，并深刻影响人们使用搜索引擎和虚拟助手的行为。

Large Language Models are highly flexible. A single model can perform a variety of vastly different tasks, such as answering questions, summarizing documents, translating languages, and completing sentences. LLMs have the potential to revolutionize content creation and profoundly impact how people use search engines and virtual assistants.

尽管并非完美无缺，但 LLM 展现出了仅需相对少量的提示或输入就能做出预测的非凡能力。LLM 是生成式人工智能的核心，能够根据人类语言输入提示生成各种形式的内容。

Although not perfect, LLMs demonstrate a remarkable ability to make predictions based on relatively small amounts of prompts or inputs. LLMs are central to Generative AI, capable of generating various forms of content based on human language input prompts.

LLM 规模庞大，能够考虑数百亿甚至数千亿个参数，因此拥有广泛的应用前景。以下是一些代表性示例：

LLMs are massive in scale, capable of considering tens or hundreds of billions of parameters, thus holding broad application prospects. Here are some representative examples:

• OpenAI 的 GPT-3：拥有 1750 亿个参数。其衍生产品 ChatGPT 能够从数据中识别模式并生成自然流畅的文本输出。
  • OpenAI's GPT-3: Has 175 billion parameters. Its derivative product, ChatGPT, can identify patterns in data and generate natural, fluent text output.
• Anthropic 的 Claude 2：虽然具体参数规模未公开，但其单次提示可处理多达 10 万个令牌，这意味着它能处理数百页技术文档甚至整本书。
  • Anthropic's Claude 2: While its specific parameter scale is not public, it can process up to 100,000 tokens in a single prompt, meaning it can handle hundreds of pages of technical documentation or even entire books.
• AI21 Labs 的 Jurassic-1：具有 1780 亿个参数和一个包含 25 万个词元（子词单元）的词汇表，具备类似的对话功能。
  • AI21 Labs' Jurassic-1: Has 178 billion parameters and a vocabulary of 250,000 tokens (subword units), with similar conversational capabilities.
• Cohere 的 Command：具备类似功能，并且支持使用 100 多种不同的语言。
  • Cohere's Command: Possesses similar functionalities and supports working with over 100 different languages.
• LightOn 的 Paradigm：提供基础模型，并宣称其性能超过 GPT-3。
  • LightOn's Paradigm: Provides foundation models and claims performance exceeding GPT-3.

所有这些 LLM 都提供 API 接口，使开发人员能够构建独特的生成式 AI 应用程序。

All these LLMs offer API interfaces, enabling developers to build unique generative AI applications.

大型语言模型如何运作？

LLM 运作原理的一个关键因素在于其表示单词的方式。早期的机器学习方法使用数字表格（如 one-hot 编码）来表示每个单词，但这种表示法无法捕捉单词之间的关系（例如语义相似性）。为了克服这一限制，现代 LLM 使用词嵌入——将单词表示为高维空间中的向量，使得语义或上下文相似的单词在向量空间中彼此靠近。

A key factor in how LLMs work lies in their method of representing words. Early machine learning methods used numerical tables (like one-hot encoding) to represent each word, but this representation couldn't capture relationships between words (e.g., semantic similarity). To overcome this limitation, modern LLMs use word embeddings—representing words as vectors in a high-dimensional space, so that words with similar semantics or contexts are close to each other in the vector space.

利用词嵌入，Transformer 的编码器首先将文本预处理为这种数值表示，从而理解语义相似的单词和短语的上下文，以及词性等其他关系。然后，LLM 的解码器可以应用这些习得的语言知识来生成新的、连贯的文本输出。

Using word embeddings, the Transformer's encoder first preprocesses text into this numerical representation, thereby understanding the context of semantically similar words and phrases, as well as other relationships like part-of-speech. Then, the LLM's decoder can apply this learned linguistic knowledge to generate new, coherent text output.

大型语言模型有哪些应用？

LLM 拥有广泛的实际应用场景。

LLMs have a wide range of practical application scenarios.

文案写作

除了知名的 GPT-3 和 ChatGPT，Claude、Llama 2、Cohere Command 和 Jurassic 等模型也能撰写原创文案。AI21 的 Wordspice 等功能则可以建议修改原始语句以改善写作风格和语气。

In addition to well-known models like GPT-3 and ChatGPT, models such as Claude, Llama 2, Cohere Command, and Jurassic can also write original copy. Features like AI21's Wordspice can suggest revisions to original sentences to improve writing style and tone.

知识库问答

这项技术通常被称为知识密集型自然语言处理（KI-NLP），指的是能够基于数字档案库中的信息帮助回答特定问题的 LLM。例如，AI21 Studio 的交互界面能够回答常识性问题。

This technology is often referred to as Knowledge-Intensive Natural Language Processing (KI-NLP), referring to LLMs that can help answer specific questions based on information in digital archives. For example, the AI21 Studio playground can answer common-sense questions.

文本分类

利用聚类等技术，LLM 可以对含义或情感相似的文本进行分类。应用包括衡量客户情绪、确定文本间关系以及文档检索。

Using techniques like clustering, LLMs can classify text with similar meanings or sentiments. Applications include measuring customer sentiment, determining relationships between texts, and document search.

代码生成

LLM 非常擅长根据自然语言提示生成代码。典型例子包括亚马逊 CodeWhisperer 和 GitHub Copilot（其底层使用了 OpenAI 的 Codex 模型），它们可以用 Python、JavaScript、Ruby 等多种编程语言编写代码。其他编码应用还包括创建 SQL 查询、编写 Shell 命令和辅助网站设计。

LLMs excel at generating code based on natural language prompts. Prominent examples include Amazon CodeWhisperer and GitHub Copilot (which uses OpenAI's Codex model underneath), capable of coding in Python, JavaScript, Ruby, and several other programming languages. Other coding applications include creating SQL queries, writing Shell commands, and assisting in website design.

文本生成

与代码生成类似，文本生成可以补全不完整的句子、编写产品文档，或者像亚马逊的 "Alexa Create" 功能那样创作简短的儿童故事。

Similar to code generation, text generation can complete incomplete sentences, write product documentation, or create short children's stories like Amazon's "Alexa Create" feature.

如何训练大型语言模型？

基于 Transformer 的神经网络规模巨大，包含多个节点和层。层中的每个节点都连接到后续层的所有节点，每个节点都有权重和偏置。这些权重、偏置以及词嵌入共同构成了模型的参数。基于 Transformer 的大型神经网络可以拥有数百亿甚至数千亿个参数。模型规模通常由模型架构、参数数量和训练数据量之间的经验关系决定。

Transformer-based neural networks are enormous in scale, containing multiple nodes and layers. Each node in a layer is connected to all nodes in subsequent layers, and each node has a weight and a bias. These weights, biases, and word embeddings together constitute the model's parameters. Large Transformer-based neural networks can have tens or hundreds of billions of parameters. Model scale is often determined by empirical relationships between model architecture, number of parameters, and training data volume.

训练过程需要使用海量的高质量数据。在训练期间，模型会迭代调整其参数值，直到能够根据先前的输入词元序列正确预测下一个词元。为此，模型采用自监督学习技术，通过调整参数来最大化在训练样本中正确预测下一个词元的可能性。

The training process requires massive amounts of high-quality data. During training, the model iteratively adjusts its parameter values until it can correctly predict the next token based on the previous sequence of input tokens. To achieve this, the model uses self-supervised learning techniques, adjusting parameters to maximize the likelihood of correctly predicting the next token in the training examples.

经过预训练后，LLM 可以很容易地通过一个称为微调的过程，使用相对较小的有标签数据集来适应执行多项特定任务。

After pre-training, LLMs can be easily adapted to perform multiple specific tasks through a process called fine-tuning, using relatively small labeled datasets.

常见的模型学习范式主要有三种：

There are three common paradigms for model learning:

• 零样本学习：基础 LLM 无需针对特定任务进行明确训练（通常仅通过提示）即可响应各种请求，但其答案的准确性可能参差不齐。
  • Zero-shot Learning: The base LLM can respond to various requests without explicit training for a specific task (often just via prompting), but the accuracy of its answers can be inconsistent.
• 少样本学习：通过提供少量相关的任务示例，基础模型在该特定领域的能力会得到显著提升。
  • Few-shot Learning: By providing a few relevant examples of the task, the base model's performance in that specific domain improves significantly.
• 微调：这是少样本学习的扩展，数据科学家使用与特定应用相关的额外数据对基础模型的参数进行进一步训练和调整。
  • Fine-tuning: This is an extension of few-shot learning, where data scientists further train and adjust the parameters of the base model using additional data relevant to a specific application.

LLM 的未来前景是什么？

随着 ChatGPT、Claude 2 和 Llama 2 等能够回答问题、生成文本的大型语言模型的出现，我们预见了一个令人兴奋的未来。可以肯定的是，LLM 将越来越接近人类的表现水平，尽管这一过程可能较为漫长。这些 LLM 迅速取得的成功，表明了人们对能够模仿甚至在某些方面超越人类思维的类人机器智能的浓厚兴趣。以下是对 LLM 未来前景的一些思考：

With the advent of Large Language Models like ChatGPT, Claude 2, and Llama 2 that can answer questions and generate text, we foresee an exciting future. It is certain that LLMs will increasingly approach human-level performance, although this process may be lengthy. The rapid success of these LLMs indicates a strong interest in human-like machine intelligence that can mimic or even surpass human thinking in certain aspects. Here are some thoughts on the future prospects of LLMs:

功能增强

尽管 LLM 已经令人印象深刻，但当前的技术水平并不完美，LLM 也并非绝对可靠。然而，随着开发人员学会如何在减少偏见和消除错误答案的同时提升性能，新版本的 LLM 将在准确性和功能上持续增强。

Although LLMs are already impressive, the current state of the art is not perfect, and LLMs are not infallible. However, as developers learn how to improve performance while reducing bias and eliminating incorrect answers, newer versions of LLMs will continue to enhance in accuracy and functionality.

多模态训练

开发人员目前主要使用文本来训练大多数 LLM，但一些人已经开始尝试使用视频和音频输入来训练模型。这种多模态训练有望加速模型开发，并为将 LLM 应用于自动驾驶汽车等领域开辟新的可能性。

Developers currently primarily use text to train most LLMs, but some have begun experimenting with training models using video and audio inputs. This multimodal training is expected to accelerate model development and open up new possibilities for applying LLMs in areas like autonomous vehicles.

工作场所变革

LLM 是一个颠覆性因素，将深刻改变工作场所。正如机器人接管了重复性的制造任务一样，LLM 可能会减少单调和重复性的脑力劳动。可能被简化的任务包括重复性文书工作、客户服务聊天机器人和简单的自动化文案撰写。

LLMs are a disruptive factor that will profoundly change the workplace. Just as robots took over repetitive manufacturing tasks, LLMs may reduce monotonous and repetitive cognitive labor. Tasks that could be streamlined include repetitive clerical work, customer service chatbots, and simple automated copywriting.

对话式 AI

LLM 无疑将提升 Alexa、Google Assistant 和 Siri 等自动虚拟助手的性能。这些助手将能够更好地理解用户意图并响应更复杂的指令。

LLMs will undoubtedly enhance the performance of automated virtual assistants like Alexa, Google Assistant, and Siri. These assistants will be better able to interpret user intent and respond to more complex commands.

AWS 如何助力大型语言模型开发？

AWS 为大型语言模型开发者提供了丰富的可能性。

AWS offers a wealth of possibilities for Large Language Model developers.

为了清晰对比 AWS 提供的核心服务，我们通过下表进行说明：

To clearly compare the core services provided by AWS, we illustrate them in the table below:

服务名称 Service Name	核心定位 Core Positioning	关键特性 Key Features
Amazon Bedrock	使用基础模型构建和扩展生成式 AI 应用的最简单方式。 The easiest way to build and scale generative AI applications with foundation models.	完全托管服务，通过 API 提供来自 Amazon 及领先 AI 公司的多种 LLM 选择。 Fully managed service offering a choice of LLMs from Amazon and leading AI companies via API.
Amazon SageMaker JumpStart	机器学习中心，提供预构建的模型和解决方案。 Machine learning hub providing pre-built models and solutions.	提供包括基础模型在内的预训练模型、内置算法；支持使用自有数据进行微调和轻松部署。 Offers pre-trained models (including FMs), built-in algorithms; supports fine-tuning with your own data and easy deployment.

Amazon Bedrock 是一项完全托管的服务，可通过 API 提供来自 Amazon 和领先 AI 初创公司的多种 LLM 选择，让您能够为特定应用场景找到最合适的模型。

Amazon Bedrock is a fully managed service that offers a choice of LLMs from Amazon and leading AI startups via API, allowing you to find the most suitable model for your specific use case.

Amazon SageMaker JumpStart 是一个机器学习中心，提供基础模型、内置算法和预构建的机器学习解决方案，只需点击几下即可部署。通过 SageMaker JumpStart，您可以访问用于执行文章摘要和图像生成等任务的预训练模型（包括基础模型）。您可以针对自己的用例，使用自有数据对这些预训练模型进行完全定制，并通过用户界面或 SDK 轻松将其部署到生产环境。

Amazon SageMaker JumpStart is a machine learning hub that provides foundation models, built-in algorithms, and pre-built machine learning solutions that can be deployed with just a few clicks. With SageMaker JumpStart, you can access pre-trained models (including foundation models) for tasks like article summarization and image generation. You can fully customize these pre-trained models for your use case using your own data and easily deploy them to production via a user interface or SDK.

立即开始您在 AWS 上探索 LLM 与 AI 的旅程。

Start your journey exploring LLMs and AI on AWS today.

常见问题（FAQ）

大型语言模型在文案写作中如何应用？

LLM基于Transformer架构，通过海量数据预训练，能理解语法和语义关联。在文案写作中，它可根据提示生成流畅文本，辅助创作广告、文章等内容，提升效率。

为什么大型语言模型能用于知识库问答？

LLM具备自主学习能力，能从文本序列中提取语义并理解关联。通过消化互联网等海量数据，模型可基于知识库内容，准确回答用户问题，实现智能问答功能。

大型语言模型在代码生成方面有什么优势？

基于Transformer的LLM并行处理输入序列，支持GPU加速训练。它能识别编程语言模式，根据自然语言描述生成代码片段，提高开发效率，是生成式AI的核心应用之一。