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Determinism II: Prompt templates and the impact on Output
The Source of Randomness in LLMs
As we all know, Large Language Models (LLMs) like OpenAI’s GPT or Google’s Gemini all rely on the now famous architecture of Generative Pretrained Transformers which at their heart rely on next word prediction. At inference, when generating this next word your input text goes through a few layers that have pre-determined randomness injected into it. Let's take a look at these layers in detail below.
Layer 1: Random Seed Initialization
At the forefront of introducing variability in LLMs is the process of random seed initialization during inference. The seed can be perceived as the starting point for the pseudo-random number generation process that underlies various stochastic operations within the model, including the aforementioned stochastic sampling. When a specific seed is used, it ensures a reproducible pattern of "randomness." This means that for a given input and a fixed seed, the model will consistently generate the same output. This consistency is paramount for applications requiring stability and predictability. However, varying the seed, even with the same input, can lead to divergent outputs, highlighting the seed's role in modulating the balance between consistency and variability in the model's responses.
Layer 2: Stochastic Sampling (Temperature-Based Sampling)
Similarly, variability in LLMs is also affected by the process of stochastic sampling, particularly temperature-based sampling. When an LLM generates text, it computes a probability distribution for the next word based on the given context. This distribution reflects how likely each word in the model's vocabulary is to follow the given sequence of words. The temperature parameter modulates this distribution. A 'temperature' in this context does not refer to physical warmth but is a metaphorical dial that adjusts the randomness in the model's choices. At a high temperature, the probability distribution becomes 'flatter', meaning the differences in likelihood between words are reduced. This encourages the model to occasionally pick less likely words, adding elements of surprise or creativity to the output. At a low temperature, the distribution is 'sharper', with the model favoring the most likely words, thus producing more predictable and conservative text.
Layer 3: Beam Search with Randomness
Beam search, particularly when infused with an element of randomness, constitutes the third layer of randomness in LLMs. During inference, beam search involves exploring multiple potential paths for the next word or sequence of words, thereby expanding the range of possible outputs. When a stochastic component is integrated, such as randomly selecting from the top-rated beams, it introduces an additional layer of unpredictability. This method not only enhances the diversity of the generated text but also provides a means to escape potential local maxima in the probability landscape, enabling the model to explore more creative or less obvious textual paths. The inclusion of randomness in beam search underscores its significance in enriching the model's generative capabilities, making it a vital tool for applications that benefit from a broader spectrum of linguistic expressions.
A key point to note is that if we always start with the same seed parameter at inference - the model will generally always reproduce the exact same answer. However, we must also realize that this only solves reproducibility, but it does not solve inherent control for how an output reacts to input perturbation - i.e., how to exactly control the output given a certain input. For more insight into this we recommend you to take a look into Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task, and Semantic Consistency for Assuring Reliability of Large Language Models.
We shall explore how we can at least tame these powerful models in the next part of our series "Determinism III: Controlling for Output Variation in LLMs"
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