In a Latent Dirichlet Allocation (LDA) model with a fixed number of topics K, you notice that nearly every document is dominated by only one or two topics, yielding very sparse document-topic distributions. You decide to retrain the model using a larger symmetric value for the Dirichlet prior α (while keeping the topic-word prior η / β unchanged).
Which outcome is this change in α most likely to produce?
Documents will tend to exhibit a more balanced mixture of several topics rather than only one or two.
The model will dynamically create additional topics beyond K, behaving like a non-parametric Dirichlet process mixture.
An L2 penalty will be added to the topic-word probability matrix, decreasing overfitting without changing sparsity.
Individual topics will now contain a more uniform mixture of most words in the vocabulary, reducing sparsity in topic-word distributions.
The Dirichlet prior α controls how topic proportions are distributed within each document. A higher (more "uniform") α places more prior mass on balanced mixtures, so inference is pushed toward documents containing several topics in relatively even proportions rather than being dominated by a single one.
Correct: Increasing α makes document-topic vectors denser (less sparse), so each document is represented by a more even mix of topics.
Distractor 1 confuses α with the topic-word prior η (β); η governs word distributions within topics, not topic mixtures within documents.
Distractor 2 describes non-parametric extensions such as a Dirichlet Process, but standard LDA with fixed K does not create new topics when α changes.
Distractor 3 mischaracterizes α as an L2 regularizer; LDA uses Dirichlet-not quadratic-priors, so no such penalty is introduced.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is the role of the Dirichlet prior α in LDA?
Open an interactive chat with Bash
How does a larger α affect topic dominance in documents?
Open an interactive chat with Bash
Why doesn’t α affect the topic-word distributions?