# SimLDA: A tool for topic model evaluation

arXiv: https://arxiv.org/abs/2208.09299 · HTML: https://mlsanity.com/paper/2208.09299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [Graph Coloring: Comparing Cluster Graphs to Factor Graphs](https://mlsanity.com/paper/2110.02048/) (2021)
- [Strengthening Probabilistic Graphical Models: The Purge-and-merge Algorithm](https://mlsanity.com/paper/2110.00091/) (2021)

## Built on by (most cited first, 1 of 1)

- [Repeatability is not recovery: Quantifying algorithmic stability and topic recovery in Latent Dirichlet Allocation](https://mlsanity.com/paper/2511.12850/) (2025)

## Abstract (arXiv)

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in the presence of limited data. We present a novel variational message passing algorithm as applied to Latent Dirichlet Allocation (LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. In situations where marginalisation leads to non-conjugate messages, we use ideas from sampling to derive approximate update equations. In cases where conjugacy holds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) is used. Our algorithm, ALBU (approximate LBU), has strong similarities with Variational Message Passing (VMP) (which is the message passing variant of VB). To compare the performance of the algorithms in the presence of limited data, we use data sets consisting of tweets and news groups. Using coherence measures we show that ALBU learns latent distributions more accurately than does VB, especially for smaller data sets.