How Do Large Language Models (LLMs) Understand Natural Language?

In recent years, large language models (LLMs) such as ChatGPT, Claude, and Gemini have entered the public eye, and their powerful natural language processing capabilities are amazing. People are beginning to wonder: do these models really "understand" language? How do they "understand" our everyday expressions? This article will delve into how LLMs process natural language from principles, training methods, and understanding mechanisms to practical examples, and clarify several common misconceptions.

What is "Understanding"? How Does Machine Understanding Differ from Human Understanding?

In the human world, language understanding relies on background knowledge, experience, logical reasoning, and emotional connections. In the context of machines, understanding refers more to "being able to correctly predict the contextual relationships of language and generate meaningful responses."

Therefore, the understanding of language by large language models is a "statistical-pattern-based" construction. It does not possess human consciousness or intention, but through vast corpora and training, it can capture the structure, logic, and context inherent in language, thereby functionally exhibiting amazing "understanding."

I. Training Basics: From Word Vectors to Transformer Architecture

1. Vectorizing Language

Before training an LLM, language first needs to be converted into a "numerical" form that machines can understand. This process is called vectorization. The most common method currently is to use word embeddings or token embeddings.

For example:

These vectors are not randomly assigned, but are learned through models, making the vector distances of semantically similar words closer. For example, the vectors of "apple" and "banana" are closer, while "tiger" is quite different from them.

2. Transformer: The Key to Capturing Context

Since Google proposed the Transformer architecture in 2017, language models have entered a period of rapid development. Through Self-Attention, Transformer enables the model to understand the relationship between each word and other words in the sentence.

Here is a visualization (pseudo-code):

Input: "The cat sat on the mat"
         ↑    ↑    ↑    ↑    ↑
        Attention weights vary (e.g., "cat" and "sat" have high weights)

This mechanism allows the model to understand "who did what to whom," i.e., syntactic and semantic structure, rather than just the concatenation of words.

II. How Are Large Language Models Trained?

1. Pre-training: Predicting the Next Word

Most language models use autoregressive training:

Given the preceding text, predict the next word.

For example:

Input: The capital of France is
Target: Paris

The model continuously repeats this task, using billions or even trillions of sentences for training. This scale allows the model to "extract knowledge" from the statistical patterns of language.

2. Fine-tuning and Instruction Tuning

After pre-training, in order to adapt to practical uses such as chatting, writing, and answering questions, it is also necessary to:

• SFT (Supervised Fine-Tuning): Humans label input-output pairs to supervise model learning;
• RLHF (Reinforcement Learning from Human Feedback): Humans score multiple answers to guide the model to be more like "human logic."

This training method makes the model more "able to understand" user needs and respond to questions in a more natural way.

III. The Core Mechanism of Language Models' "Understanding" of Language

1. Context Modeling Ability

Large models do not understand the words themselves, but rather the relationships between words. For example:

• Word order: who is in front, who is behind
• Synonym replacement: the ability to recognize the same meaning behind different expressions
• Context maintenance: whether the logic of the previous text is retained in long conversations

For example, in answering:

"What are some tragedies written by Shakespeare?"

The model will associate:

• "Shakespeare" ⇒ writer, drama, tragedy
• "Tragedy" ⇒ Hamlet, Macbeth, Othello, and other works

This is not because it memorized a certain answer, but because it learned the co-occurrence relationship of these words from massive amounts of text.

2. Modality Transfer and Abstract Reasoning

As the model parameters increase, it gradually possesses a certain "abstract ability," such as:

• Understanding analogy relationships: "Cat is to kitten, as dog is to what?"
• Inferring situations: "If it rains today, I won't go." ⇒ judging whether to go based on the weather
• Generating multi-turn dialogues: continuously outputting appropriate content by combining the context before and after

IV. Real Case Analysis

Case 1: "Contextual Understanding" in Language Translation

Input:

"He saw her duck."

This sentence may have two meanings:

• He saw her duck (duck is a noun)
• He saw her duck down (duck is a verb)

The language model judges which meaning it is through context. Experiments have found that large models such as GPT-4 can choose the correct semantics in 92% of ambiguous sentence disambiguation tasks, while traditional translation systems can only do so in 63%.

Case 2: Medical Consultation Assistance

Researchers trained LLMs on millions of medical documents on PubMed, and the results showed that:

• The accuracy rate of basic disease identification increased to 87%
• In terms of symptom recommendation and follow-up inquiries, the performance is close to that of intern doctors

This shows that the model can "understand" terminology, reasoning processes, and pathological logic from professional corpora.

V. Common Misconceptions: LLMs Do Not Truly "Understand"

1. No self-awareness: The model does not "know" what it is saying.
2. Cannot build a world model: It lacks direct perception of real-world entities and physical laws.
3. Prone to hallucinations: When lacking knowledge, the model tends to "fabricate" answers.

Therefore, its "understanding" is a probabilistic, predictive behavior, and its essence is still pattern recognition.

VI. Outlook: The Boundaries of Understanding Are Being Broken Through

Although language models do not truly "understand language," they are demonstrating processing capabilities that exceed the human average level in an increasing number of tasks.

Future development directions include:

• Multimodal understanding (language + image + voice)
• Enhancing logic and reasoning ability
• Introducing world knowledge (knowledge graph + RAG technology)
• Strengthening memory mechanisms (such as long context windows, external memory systems)

These advances will make LLMs closer to "human-like understanding."

VII. Conclusion

The reason why large language models can "understand" language is because they have captured the underlying patterns of language with unprecedented scale and algorithms. From literal semantics to contextual understanding, from simple dialogues to complex reasoning, they are constantly approaching the core logic of human language.

Understanding is not a "human-exclusive" ability, but a complex mapping and induction process. In this regard, LLMs are "learning the truth of language" in another way.

? They don't understand language, but they have shocked the world with language.