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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAutoregressive large language models predict text by turning the context so far into scores for possible next tokens. A decoding rule selects one token, adds it to the context, and the model repeats the process. Training adjusts the model’s parameters to make its predictions fit example sequences. This describes a common GPT-style mechanism—not every language model or every part of a deployed AI assistant.
What is a token?
A token is a unit from the model’s vocabulary. It may represent a whole word, part of a word, or a single character, so “next token” is more precise than “next word.” Google’s Machine Learning Crash Course explains that LLMs predict tokens or sequences of tokens, potentially extending across many paragraphs.
Tokenization happens before the model processes text. The model operates on the resulting sequence of tokens, not on words as people intuitively divide them.
How does an autoregressive model predict the next token?
- It processes the context. The prompt and any tokens already generated are represented as a sequence. In a transformer, self-attention lets representations incorporate information from other positions in that context, while stacked layers process those representations successively. Attention is a computational mechanism; it should not be taken as literal human attention or proof that a particular attention head has one simple, fixed meaning.
- It scores possible continuations. At the current sequence position, the model’s language-model output produces a score, called a logit, for each token in its vocabulary. In ordinary generation, the final position’s scores are used to choose what comes next. Hugging Face’s OpenAI GPT documentation describes this implementation detail.
- A decoding rule selects a token. A system can choose a high-scoring option or sample among options according to its decoding setup. Scores describe relative support for candidates; they do not establish a uniquely correct continuation.
- The selected token joins the context. The model then predicts again using the updated sequence. Repeating this loop produces a longer response, one token at a time.
Researchers describe transformer training as learning to predict the next token given an input sequence; see the abstract for “Mechanics of Next-Token Prediction with Transformers,” presented at AISTATS 2024.
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How does training differ from generating a response?
During training, example sequences supply next-token targets. The model’s prediction is compared with the target using a loss, and an optimization procedure updates its parameters to reduce prediction error. In the documented OpenAI GPT implementation, labels are shifted so each position is trained to predict the next token. This is a training objective, not the same operation as choosing tokens during a live response.
OpenAI describes its models’ parameters, or weights, as numerical values adjusted during training to reflect patterns in data, and says generation uses those learned weights. That is different from looking up a stored “next sentence,” though it does not establish a universal guarantee that a model can never reproduce memorized material.
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Why can the same question get different answers?
A context can support several plausible next tokens, and the selection policy can affect which one is emitted. Sampling can introduce variation; other decoding choices may favor higher-scoring candidates. As OpenAI notes in its text-generation explanation, outputs can vary because multiple continuations may be plausible. Exact behavior depends on the model and the deployment’s decoding settings.
Does next-token prediction explain everything an AI assistant does?
No. Next-token prediction is a central mechanism in autoregressive base models, but it is not a complete account of assistant behavior. Training and product layers can shape how a model responds, and deployed systems may also use tools or other components.
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For example, OpenAI’s GPT-4 research page says its base model was trained to predict the next word in a document, then describes reinforcement learning from human feedback (RLHF) as a way to steer behavior toward user intent within guardrails. That is OpenAI’s description of GPT-4, not evidence that every provider uses the same post-training recipe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do all large language models predict the next token?
No. The explanation here applies to autoregressive, GPT-style models. Other language-model objectives exist: Google’s Machine Learning Crash Course distinguishes predicting a following token from masked-token training, where a model learns to predict a missing token in context. The phrase “LLM predicts the next token” is therefore useful shorthand for a common design, not a universal definition of every LLM.
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