The Latent Dirichlet Allocation (LDA) topic model is a generative model, that is it mimics (but in a simplified way) how humans write documents:

Question

BlackTom AI · Accepted Answer

Begin by unpacking what the statement is claiming about LDA. The claim states that LDA is a generative model, and further that it mimics, in a simplified way, how humans write documents.
Option 1 (True): This aligns with the standard characterization of LDA. LDA is defined as a generative probabilistic model in which each document is assumed to be produced by a mixture of topics, and each topic is a distribution over words. This framework models the document generation process and can be viewed as a simplified abstraction of how writers select topics and words when composing text. The word distributions for topics and the topic mixtures for documents are governed by Dirichlet priors, which provides regularization and tractability. The description “mimics (but in a simplified way) how humans write documents” captures the intuition that humans compose text by drawing from topics and word choices, though clearly it is a stylized, probabilistic, and computationally tractable approximation rather than a faithful replica of human cognition.
Option 2 (False): If one were to claim LDA is not a generative model, this would be incorrect because the core formulation explicitly defines a generative process: sample topics from a Dirichlet prior, then sample topic assignments and words for each document from those topic distributions. The claim that it mirrors human writing in a literal sense would also be overly strong; while the model provides a plausible generative story, it cannot capture all nuances of real writing, such as syntax, discourse structure, or pragmatics. However, these points do not negate its status as a generative model, nor the general interpretation that it captures a simplified version of how topics influence word choices in documents.
In summary, the essential idea is that LDA is indeed a generative probabilistic model of documents, designed as a simplified abstraction of topic-driven writing processes. The false option would be incorrect because it denies the generative nature or mischaracterizes the level of abstraction, whereas the true option accurately reflects the formal definition and the intended intuition behind LDA.

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

The Latent Dirichlet Allocation (LDA) topic model is a generative model, that is it mimics (but in a simplified way) how humans write documents:

View Explanation

Log in for full answers

Similar Questions

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

Who decides on the number of topics in the Latent Dirichlet Allocation (LDA) topic model?

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:

The Latent Dirichlet Allocation (LDA) topic model accepts data in the following format (choose one best answer):

The topics are latent in the Latent Dirichlet Allocation topic model and therefore have to be inferred from the data:

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

The Latent Dirichlet Allocation (LDA) estimates the distribution of topics in documents and the distribution of words in topics

What is topic modeling? Choose one best answer:

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler