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A language model generates text one token at a time. But how does it know when to stop?
It doesn't understand completion in the same way a human does. Instead, it learns patterns that help it predict when a response should end. Special tokens, instructions, and generation settings all play a role in this process.
Let's explore how it works.
1. How a Model Generates Text
Each time a language model generates text, it predicts the next token based on the tokens that came before it.
A token can represent a word, part of a word, punctuation, or another piece of text. The model assigns probabilities to possible next tokens and selects one according to its generation settings.
One possible token is a special marker that signals the end of generation. This is commonly called an end-of-sequence (EOS) token. Depending on the model, it might look like </s> or <|endoftext|>.
When the model generates an EOS token, generation normally ends. In chat models, a separate end-of-turn token may serve this purpose by indicating that the assistant has finished its response.
2. How a Model Learns to Stop
During training, models learn to predict the next token from examples of text. Special tokens are included in the training data to mark boundaries, such as the end of a sequence or a conversational turn.
For example, a training sequence might look like this:
User: What is the capital of France?
Assistant: Paris. [end-of-turn token]
By learning from many examples, the model learns when these ending tokens tend to appear.
After generating a complete answer, the model may assign a high probability to an ending token. However, the model does not explicitly verify that it has answered the question correctly or included every necessary detail. It predicts the next token based on patterns learned during training.
The exact behavior depends on how the model was trained and which ending tokens it uses.
3. What Influences When a Model Stops?
Several factors influence whether a model generates another token or ends its response.
Context
The model uses the preceding text to predict what should come next. If the response resembles patterns associated with a completed answer, an ending token may become more likely.
However, a response can appear complete to a reader without the model actually selecting an ending token.
Instructions
Instructions such as "Answer in one sentence" or "Keep your response under 100 words" can influence the length of the generated response.
Fine-tuning methods, including reinforcement learning from human feedback (RLHF), can also shape how a model responds and when it tends to finish.
These factors influence the model's behavior, but they do not guarantee a particular response length.
Sampling settings
Generation settings affect how the model selects its next token.
- Temperature: Adjusts how strongly the model favors higher-probability tokens over lower-probability ones.
- Top-p: Restricts sampling to a set of tokens whose cumulative probability reaches a specified threshold.
These settings can affect whether an ending token is selected, particularly when it competes with other plausible next tokens.
4. External Conditions That Stop Generation
Not every response ends because the model generates an ending token. The application running the model can also stop generation.
Common external stopping conditions include:
- Maximum token limit: The application sets a maximum number of tokens the model can generate. Once the limit is reached, generation stops even if the response is incomplete.
-
Stop sequences: The application can specify text sequences that trigger a stop when detected. For example, an application might stop when it encounters
"\n\nUser:". - Context window limits: The model can only process a limited amount of text at once. If the available context or generation capacity is exhausted, the application may be unable to continue generating.
These mechanisms are different from the model selecting an EOS token. They are stopping conditions imposed by the model's runtime or application.
5. Common Stopping Problems
Understanding the difference between model-generated endings and external stopping conditions helps explain several common problems.
Rambling or repetitive responses
The model continues generating because it keeps predicting additional tokens instead of selecting an ending token. It may repeat earlier points, introduce unnecessary details, or continue beyond what the user needs.
Premature stopping
The model generates an ending token before fully answering the question. This can happen even when the response is incomplete because the model predicts that the sequence should end.
Truncation
The application reaches its maximum generation limit and stops the response. The output may end mid-sentence, making it appear as though the model decided to stop on its own.
The distinction matters: premature stopping comes from the model's generated ending, while truncation occurs because an external limit was reached.
Conclusion
A language model does not have a reliable, human-like understanding of when an answer is complete. It learns patterns that help it predict when to generate an ending token, while instructions and sampling settings influence its behavior. External limits can also stop generation independently of the model's predictions.
This distinction matters when building AI applications. If a model stops too early, users may receive incomplete answers. If it continues for too long, it can increase token usage, latency, and cost.
Understanding these mechanisms helps developers configure generation settings, choose appropriate token limits, and diagnose whether a response ended because the model generated an ending token or the application stopped it.
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