The Latent Dirichlet Allocation (LDA) topic model needs an initial guess for the distribution of topics in documents and distribution of words in each topic:

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BlackTom AI · Accepted Answer

The question asks about what LDA requires as a starting point for its algorithms. 
Option 1: True. LDA relies on initial conditions to begin inference. In many implementations, you start with priors over topic proportions for documents (often Dirichlet with parameter alpha) and over word distributions for topics (often Dirichlet with parameter beta), which can be viewed as initial guesses about the distributions. Some methods also begin by randomly assigning topics to words to bootstrap Gibbs sampling, effectively providing an initial topic assignment that's refined later. 
Option 2: False. If you interpret the statement as requiring something other than an initial guess, you'd be mistaken. LDA does not magically know the topic structure a priori; it must start from some initialization to perform inference, whether via randomly assigned topics, symmetric priors, or other initialization schemes. 
In summary, since LDA needs starting values for document-topic and topic-word distributions (or a random topic assignment to begin iterative inference), the claim in Option 1 is correct, and Option 2 is incorrect.

SP2025.B69.DAT.562.24 COMPLETE Quiz #4 AFTER VIDEO 3: Latent Dirichlet Allocation (LDA)

The Latent Dirichlet Allocation (LDA) topic model needs an initial guess for the distribution of topics in documents and distribution of words in each topic:

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