When working with language models such as those provided by Hugging Face or other AI libraries, we can significantly influence the generated text by adjusting different generation parameters.
First, it’s important to distinguish between two types of parameters:
Model parameters, which are defined during the training phase
Generation parameters, which control how text is produced after the model has been trained
Maximum Number of Tokens
One of the most commonly used parameters is the maximum number of tokens the model is allowed to generate. In Hugging Face, this parameter is called max_length.
A higher value allows the model to generate longer text. However, keep in mind that max_length defines a maximum limit, not a guaranteed length. The generation process may stop earlier if the model produces a special end-of-sequence (EOS) token.
This mechanism prevents the model from generating unnecessary or overly long output.
Greedy Decoding
Greedy decoding is one of the simplest text generation strategies.
At each step, the model selects the token with the highest probability and appends it to the sequence, repeating the process until completion.
Pros:
Deterministic and consistent output
High coherence
Cons:
Often repetitive
Lacks creativity and diversity
Because of these limitations, greedy decoding is usually not ideal for creative text generation.
Multinomial Sampling
To introduce variability and creativity, we can use multinomial sampling.
With this approach, tokens are sampled randomly based on the probability distribution produced by the softmax layer. More probable tokens are still more likely to be selected, but less probable ones have a chance as well.
This results in more diverse and natural-sounding text.
Beam Search
Beam search is a more advanced decoding strategy that generates multiple sequences in parallel.
At each step, the algorithm keeps the top n best sequences (called beams). Each beam is expanded and evaluated, and the best overall sequence is selected at the end.
Beam search is especially useful in tasks like machine translation, where accuracy and coherence are critical.
Top-K Sampling
The top_k parameter restricts token selection to the K most probable tokens.
For example, setting top_k = 3 means the next token will be chosen only from the top three candidates.
Larger top_k values increase creativity but may reduce coherence.
Top-P (Nucleus Sampling)
Top-p sampling, also known as nucleus sampling, selects tokens based on their cumulative probability.
Only tokens whose cumulative probability exceeds a given threshold (for example 0.9) are considered. The number of eligible tokens adapts dynamically to the probability distribution.
This method often provides a better balance between diversity and coherence than top-k sampling.
Temperature
The temperature parameter reshapes the probability distribution of the output tokens.
A low temperature (less than 1) produces more deterministic and conservative output, while a high temperature (greater than 1) encourages more creative and diverse text.
Adjusting the temperature is one of the most effective ways to control randomness in text generation.
Using Generation Parameters with Hugging Face
In Hugging Face, these parameters can be configured directly when calling the generation function.
The do_sample=True option enables multinomial sampling, num_beams activates beam search, and parameters such as top_k, top_p, and temperature allow you to fine-tune creativity and randomness.
By carefully combining these parameters, you can tailor the behavior of a language model to meet your specific use case, whether you need precise, structured text or creative, open-ended output.