The Latent Dirichlet Allocation (LDA) approach to topic modeling assumes that:

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BlackTom AI · Accepted Answer

The question concerns the Latent Dirichlet Allocation (LDA) approach to topic modeling and what it assumes about the number of topics.
Option 1: 'The number topics is not known' — In standard LDA formulations, the number of topics K is a hyperparameter that the user must specify before fitting the model. While model selection methods can be used to help choose K, the core assumption of classic LDA is not that K is unknown to the model during inference; rather, K is fixed and provided as input. Therefore, this statement misstates the typical assumption of LDA with respect to K.
Option 2: 'The number of topics is known' — This aligns with the common practice in LDA where the analyst selects a value K (the number of topics) prior to running the model. The inference then discovers topic distributions and word distributions conditioned on that fixed K. Although in practice one might test multiple K values to find a suitable number of topics, the algorithm itself operates under the assumption that K is known (specified) for each run.
Additional nuance: In Bayesian treatment, one could place priors over the topic structure that allow for a nonparametric extension (e.g., HDP—Hierarchical Dirichlet Process) where the number of topics is not fixed in advance. However, vanilla LDA as originally formulated assumes a known K and performs inference conditioned on that choice. Thus, overall, the statement that matches the standard LDA setup is that the number of topics is known at the time of modeling, even though in practice you may explore multiple K values to select an appropriate count.

SP2025.B69.DAT.562.24 COMPLETE Quiz #2 AFTER VIDEO 1: LDA Overview

The Latent Dirichlet Allocation (LDA) approach to topic modeling assumes that:

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The output of the Latent Dirichlet Allocation (LDA) topic model is:

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