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I keep reading about how the latest and greatest LLMs have billions of parameters. As someone who is more familiar with standard neural nets but is trying to better understand LLMs, I'm curious if a LLM parameter is the same as a NN weight i.e. is it basically a number that starts as a random coefficient and is adjusted in a way that reduces loss as the model learns? If so, why do so many researches working in the LLM space refer to these as parameters instead of just calling them weights?

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Yes, the parameters in a large language model (LLM) are similar to the weights in a standard neural network. In both LLMs and neural networks, these parameters are numerical values that start as random coefficients and are adjusted during training to minimize loss. These parameters include not only the weights that determine the strength of connections between neurons but also the biases, which affect the output of neurons. In a large language model (LLM) like GPT-4 or other transformer-based models, the term "parameters" refers to the numerical values that determine the behavior of the model. These parameters include weights and biases, which together define the connections and activations of neurons within the model. Here's a more detailed explanation:

  • Weights: Weights are numerical values that define the strength of connections between neurons across different layers in the model. In the context of LLMs, weights are primarily used in the attention mechanism and the feedforward neural networks that make up the model's architecture. They are adjusted during the training process to optimize the model's ability to generate relevant and coherent text.

  • Biases: Biases are additional numerical values that are added to the weighted sum of inputs before being passed through an activation function. They help to control the output of neurons and provide flexibility in the model's learning process. Biases can be thought of as a way to shift the activation function to the left or right, allowing the model to learn more complex patterns and relationships in the input data.

The training process involves adjusting these parameters (weights and biases) iteratively to minimize the loss function. This is typically done using gradient descent or a variant thereof, such as stochastic gradient descent or Adam optimizer. The loss function measures the difference between the model's predictions and the true values (e.g., the correct next word in a sentence). By minimizing the loss, the model learns to generate text that closely resembles the patterns in its training data.

Researchers often use the term "parameters" instead of "weights" to emphasize that both weights and biases play a crucial role in the model's learning process. Additionally, using "parameters" as a more general term helps communicate that the model is learning a complex set of relationships across various elements within the architecture, such as layers, neurons, connections, and biases.

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    $\begingroup$ Thanks for the response! Genuinely curious - was this response generated by an LLM? It reads like so. $\endgroup$ Commented Jun 21, 2023 at 20:49

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