A researcher is comparing two language models. Model A is defined by a set of parameters $\theta_A$. Model B is a version of Model A that has been fine-tuned on a new dataset, resulting in a new set of parameters, $\theta_B$. The researcher wants to compare the probability of each model generating the word 'innovative' given the same input context and using the same sampling strategy, $s$. Which of the following mathematical expressions accurately represents this comparison?

An AI engineer is working with a pre-trained Large Language Model, whose probability distribution is represented by $Pr_\theta^s(\cdot)$. The engineer decides to change the method used to select the next word from the model's output probabilities, switching from a greedy approach to a top-k sampling approach. The model's underlying weights and biases are not modified. Which component of the notation $Pr_\theta^s(\cdot)$ would need to be updated to reflect this change?

Policy Notation for Autoregressive Models (π_θ)

Analyzing Model Update Notation