When interacting with Large Language Models (LLMs) like GPT, BERT, or T5, the magic happens not only in the training but also in the decoding phase. Decoding is the process through which a language model generates output, and the choice of decoding method can greatly influence the quality of the generated text.
In this post, we’ll explore the common decoding techniques, explain key parameters such as top-p and top-k, and discuss how these models are trained to generate coherent and contextually accurate outputs.
1. Introduction to Decoding in LLMs
Decoding is the step where the model takes a sequence of probabilities generated by its neural network and converts it into human-readable text. This is not a straightforward task, as the model can produce many possible sequences. Choosing the right sequence involves controlling randomness and ensuring that the output is grammatically and contextually correct.
Here are the most common decoding techniques:
1.1. Greedy Decoding
Greedy decoding selects the word with the highest probability at each step of generation. While simple, it can often result in repetitive or low-quality text because it doesn’t explore alternative words that might lead to better outcomes.
Example:
- Input: “The cat”
- Greedy Output: “The cat is on the mat.”
1.2. Beam Search
Beam search is an improvement over greedy decoding. It keeps track of multiple possible sequences (called “beams”) at each step, choosing the best one after considering several alternatives. The size of the beam (beam width) determines how many sequences the model will consider.
Example:
- Input: “The cat”
- Beam Search Output: “The cat is sitting by the window.”
Beam search provides more coherent and higher-quality outputs, but it can be computationally expensive.
1.3. Top-k Sampling
Top-k sampling is a method where the model only considers the top k most probable words at each step, and it randomly selects one of them. This introduces more diversity and creativity in the output by not always choosing the most probable word.
k: The number of top words to sample from.
Example:
- Input: “The cat”
- Top-k (with k=5) Output: “The cat jumped over the fence.”
The higher the value of k, the more diverse and unexpected the text can become, but it might also lose coherence.
1.4. Nucleus Sampling (Top-p Sampling)
Top-p sampling, also known as nucleus sampling, improves on top-k by considering all words whose cumulative probability adds up to a threshold p. It adapts dynamically to the context of the generation, ensuring both diversity and coherence.
p: The cumulative probability threshold (e.g., p=0.9).
Example:
- Input: “The cat”
- Top-p (with p=0.9) Output: “The cat darted across the room.”
Top-p sampling adjusts the number of words sampled at each step, giving the model more flexibility compared to top-k, where the number of options is fixed.
1.5. Temperature Sampling
Temperature controls the randomness of the output by scaling the probability distribution of the model. Higher temperatures make the model more likely to select less probable words, resulting in more creative but potentially less coherent text.
temperature: A value between 0 and 1 (default is 1). Lower values make the output more deterministic, while higher values increase randomness.
Example:
- Input: “The cat”
- Temperature 0.5: “The cat is sleeping.”
- Temperature 1.5: “The feline leaps across the horizon in pursuit of adventure.”
A lower temperature makes the model more conservative, while a higher temperature makes it more creative.
2. Key Parameters in Decoding
Now that we understand the common decoding methods, let’s look at the parameters that control how these methods function and affect the quality of generated text.
2.1. top-k
top-k is a parameter used in top-k sampling, determining the number of top possible words to sample from at each step. It balances between randomness and coherence.
- Low
kvalues: Result in more deterministic and repetitive text. - High
kvalues: Lead to more varied, creative text but may decrease coherence.
When to use?: Use top-k sampling when you want more diverse outputs, like in creative writing tasks or when generating longer texts.
2.2. top-p (Nucleus Sampling)
top-p is the cumulative probability threshold for selecting words in nucleus sampling. Unlike top-k, it adapts dynamically to the output distribution.
- Low
pvalues: Lead to conservative text, similar to greedy decoding. - High
pvalues: Increase diversity by sampling from a larger set of possible words.
When to use?: Nucleus sampling is great for balancing quality and creativity, especially for conversational agents or storytelling.
2.3. temperature
temperature affects the randomness of word selection by scaling the probabilities in the output distribution.
- Lower temperature (e.g., 0.2): More deterministic and focused outputs.
- Higher temperature (e.g., 1.2): More creative but less predictable outputs.
When to use?: Adjust the temperature to match your needs. For factual, structured responses, a low temperature is preferred. For creative or open-ended generation, a higher temperature can lead to more varied responses.
2.4. max_length
max_length defines the maximum number of tokens the model can generate. This limits how long the output will be and is often used to prevent overly long or runaway generations.
- Short
max_length: Produces concise and to-the-point responses. - Long
max_length: Useful for generating essays, articles, or other long-form content.
3. How Are LLMs Trained?
Understanding decoding methods and parameters is critical for using LLMs effectively, but how are these models trained to generate such coherent and contextually accurate text? Let’s briefly cover the training process of LLMs.
3.1. Pretraining Phase
LLMs like GPT or BERT are trained on massive datasets containing text from the web, books, and other sources. During pretraining, the model learns to predict the next word in a sentence (for models like GPT) or predict masked words (for models like BERT). This helps the model develop a strong understanding of language patterns, syntax, and semantics.
3.2. Fine-Tuning Phase
After pretraining, the model is fine-tuned on specific tasks, such as question answering, summarization, or text generation. Fine-tuning is done on a smaller, more specific dataset related to the task at hand. For instance, to fine-tune a model for customer service chatbots, it would be trained on dialogue data from customer interactions.
3.3. Loss Function
The loss function during training helps the model learn from its mistakes. For LLMs, the most common loss function is cross-entropy loss, which measures how well the model’s predicted probability distribution matches the true distribution of the next word.
3.4. Optimizers and Training Dynamics
To optimize the model’s parameters, techniques like Adam or AdamW (Weight Decay) are used. These algorithms help the model converge to a solution where the predictions match the real-world language distribution as closely as possible.
The training process involves:
- Forward Pass: The model generates predictions.
- Backpropagation: Errors (measured by the loss function) are propagated back through the model to adjust its parameters.
- Gradient Descent: The optimizer updates the model’s weights to minimize the loss.
4. Conclusion
The decoding phase of Large Language Models is where the raw power of the model is transformed into readable, meaningful text. By selecting the right decoding method (greedy, beam search, top-k, or top-p) and adjusting key parameters like temperature, top-k, and top-p, you can influence the creativity, coherence, and quality of the generated output.
Understanding these parameters allows you to fine-tune the model’s behavior for different tasks, from factual reporting to creative writing. Along with proper training, these methods ensure that LLMs deliver contextually accurate and relevant text.
