How does a neural network help an LLM predict the next token?

How does a neural network help an LLM predict the next token?

A neural network helps an LLM predict the next token by transforming the available text into a representation used to score possible continuations. Its learned parameters influence how the preceding context affects those scores. The prediction concerns a next text unit, which may be a word fragment rather than a whole word.

Represent the available context

The tokenizer converts text into IDs, and an embedding layer supplies initial vectors. Position information helps the model use order. Transformer blocks then combine information across allowed positions and apply learned transformations to produce richer hidden representations.

For the prompt “The baker placed the bread in the,” the network can use the subject, action, and nearby nouns to favor some continuations over others. This example illustrates contextual prediction without claiming a particular model’s actual probabilities.

Produce scores across the vocabulary

An output projection maps the relevant final hidden representation to a score, or logit, for each vocabulary item. A softmax operation can convert these scores into a probability distribution. The generation algorithm then uses that distribution, or transformed scores, to choose the next token.

Training shapes this process by adjusting parameters to improve predictions on examples. The network is not ordinarily searching a table containing every possible sentence.

Extend the sequence and repeat

After a token is selected, it becomes part of the context for subsequent generation. If the model selects a piece corresponding to “oven,” the following prediction uses that new continuation. A plausible sequence can still contain factual mistakes, so high next-token probability should not be interpreted as a direct measurement that the completed statement is true.