What is backpropagation in the context of training a language model?

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BlackTom AI · Accepted Answer

Reiterating the question: What is backpropagation in the context of training a language model?
Option 1: 'The process of manually setting parameters' — This is incorrect because backpropagation is not about manual parameter setting; it automates parameter updates based on gradients rather than direct human adjustment, so this statement mischaracterizes the mechanism.
Option 2: 'A technique for tweaking parameters after incorrect guesses' — This is the essence of backpropagation. It uses gradient descent to adjust model parameters in order to reduce error after comparing the model's output to the true target, guiding learning through computed gradients.
Option 3: 'A method of inputting data' — Backpropagation does not concern the way data is fed into the network; it operates on the internal flow of error signals to update weights, so this option confuses data handling with parameter optimization.
Option 4: 'The creation of initial training datasets' — Creating datasets is an upstream data preparation step, not a learning mechanism like backpropagation, which happens during training after data is presented and errors are measured.
In summary, option 2 correctly captures the core idea of backpropagation as the gradient-based parameter adjustment process that follows error feedback to improve the model's predictions, while the other options describe activities unrelated or only tangentially related to how backpropagation functions in training a language model.

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What is backpropagation in the context of training a language model?

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