Seed vs. Temperature in Language Models
Understanding how these two key parameters shape reproducibility in text generation
In the world of large language models (LLMs), two key parameters — seed and temperature — shape how text is generated, influencing whether the output is consistent or varied, predictable or creative. While both might initially seem to control the randomness of a model's output, they serve distinct purposes and affect the model's behavior in very different ways. So, how do they differ?
Seed
The seed is essentially the starting point for the random number generator the model uses during text generation. When the temperature is greater than 0, randomness is introduced into the selection of the next token. Instead of always choosing the most likely next word, the model picks from a range of possibilities, with the probabilities of each token influenced by the temperature setting. The seed ensures that this randomness is reproducible — meaning that with the same seed, prompt, and settings (including temperature), the output will always be the same.
For example, if the model is tasked with completing:
The mouse ate the [ ]
And the possible tokens are:
['cheese', 'sandwich', 'apple', 'carrot', 'cookie']
With a non-zero temperature (temperature > 0), the model might generate::
seed = 41:"carrot"The mouse ate the [carrot]
seed = 42:"cheese"The mouse ate the [apple]
Changing the seed produces different outputs, but using the same seed (e.g., 42) will always yield the same result ("cheese").
Temperature
Temperature controls how deterministic or random the model's token selection process is. At a zeroed temperature (temperature = 0), the model becomes fully deterministic, always selecting the most probable token based on its training data. In this mode, changing the seed has no effect because there’s no randomness for the seed to influence.
Revisiting the same example:
The mouse ate the [ ]
With the same list of possible tokens:
['cheese', 'sandwich', 'apple', 'carrot', 'cookie']
By setting temperature = 0, the model will consistently choose the most likely token, such as:
'cheese'
This deterministic behavior means that regardless of the seed, the output will always be:
seed = 41:"cheese"
The mouse ate the [cheese]
seed = 42:"cheese"
The mouse ate the [
cheese]
So, since the temperature is 0, the seed does not influence the outcome, and the model will always choose the most probable token regardless of the seed value.
Consistency vs. Determinism for Reproducibility
The relationship between these parameters can be summarized in terms of consistency and determinism:
Seed: Provides consistency in scenarios where randomness is involved (temperature > 0). It ensures that results are reproducible even when there are multiple valid outputs. While different seeds yield different results, using the same seed ensures identical outputs every time.
Temperature: Controls whether randomness is introduced at all. A temperature of 0 guarantees determinism, ensuring the output is always the same for a given input, regardless of the seed.
So, long story short: Both settings contribute to reproducibility but from different angles. The seed controls how randomness plays out when it’s present (i.e., when temperature > 0), ensuring that random processes are consistent across runs. Meanwhile, temperature controls whether randomness exists at all — when set to zero, it removes randomness entirely and makes the generation process fully deterministic.