# LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models

arXiv: https://arxiv.org/abs/2406.09008 · HTML: https://mlsanity.com/paper/2406.09008/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 75 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 30 of 38)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [OTA: Optimal Transport Assignment for Object Detection](https://mlsanity.com/paper/2103.14259/) (2021)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse](https://mlsanity.com/paper/1911.02469/) (2019)
- [Discovering Discrete Latent Topics with Neural Variational Inference](https://mlsanity.com/paper/1706.00359/) (2017)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Neural Topic Model via Optimal Transport](https://mlsanity.com/paper/2008.13537/) (2020)
- [Contrastive Learning for Neural Topic Model](https://mlsanity.com/paper/2110.12764/) (2021)
- [Neural Models for Documents with Metadata](https://mlsanity.com/paper/1705.09296/) (2017)
- [A Unified Wasserstein Distributional Robustness Framework for Adversarial Training](https://mlsanity.com/paper/2202.13437/) (2022)
- [Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings](https://mlsanity.com/paper/2203.01570/) (2022)
- [Transformed Distribution Matching for Missing Value Imputation](https://mlsanity.com/paper/2302.10363/) (2023)
- [Vector Quantized Wasserstein Auto-Encoder](https://mlsanity.com/paper/2302.05917/) (2023)
- [Gamma Belief Networks](https://mlsanity.com/paper/1512.03081/) (2015)
- [Learning Prototype-oriented Set Representations for Meta-Learning](https://mlsanity.com/paper/2110.09140/) (2021)
- [Contextualized Topic Coherence Metrics](https://mlsanity.com/paper/2305.14587/) (2023)
- [Zero-Shot Cross-Lingual Summarization via Large Language Models](https://mlsanity.com/paper/2302.14229/) (2023)

## Built on by (most cited first, 11 of 11)

- [Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation](https://mlsanity.com/paper/2502.07352/) (2025)
- [Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Models](https://mlsanity.com/paper/2509.07142/) (2025)
- [MALTopic: Multi-Agent LLM Topic Modeling Framework](https://mlsanity.com/paper/2601.15299/) (2026)
- [Iterative Improvement of an Additively Regularized Topic Model](https://mlsanity.com/paper/2408.05840/) (2024)
- [Knowing Your Uncertainty -- On the application of LLM in social sciences](https://mlsanity.com/paper/2512.05461/) (2025)
- [Improving Topic Modeling by Distilling Soft Labels from Language Models](https://mlsanity.com/paper/2602.17907/) (2026)
- [A Neural Topic Method Using a Large-Language-Model-in-the-Loop for Business Research](https://mlsanity.com/paper/2603.03623/) (2026)
- [PRISM: PRIor from corpus Statistics for topic Modeling](https://mlsanity.com/paper/2603.29406/) (2026)
- [AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models](https://mlsanity.com/paper/2606.28708/) (2026)
- [Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities](https://mlsanity.com/paper/2607.03887/) (2026)
- [Document Topic Alignment Metrics for Evaluating Topic Models of Short-Text Public Health Communications on Social Media](https://mlsanity.com/paper/2609.14256/) (2026)

## Abstract (arXiv)

Topic modeling has been a widely used tool for unsupervised text analysis. However, comprehensive evaluations of a topic model remain challenging. Existing evaluation methods are either less comparable across different models (e.g., perplexity) or focus on only one specific aspect of a model (e.g., topic quality or document representation quality) at a time, which is insufficient to reflect the overall model performance. In this paper, we propose WALM (Word Agreement with Language Model), a new evaluation method for topic modeling that considers the semantic quality of document representations and topics in a joint manner, leveraging the power of Large Language Models (LLMs). With extensive experiments involving different types of topic models, WALM is shown to align with human judgment and can serve as a complementary evaluation method to the existing ones, bringing a new perspective to topic modeling. Our software package is available at https://github.com/Xiaohao-Yang/Topic_Model_Evaluation.