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

2024-06-13

arXiv.org

AI Summary

Existing topic model evaluation metrics—such as perplexity—suffer from fundamental limitations: poor cross-model comparability, unidimensionality, and misalignment with human judgments, hindering comprehensive quality assessment. To address these issues, we propose WALM—the first LLM-based *joint* evaluation framework for topic models. WALM simultaneously quantifies both *topic semantic quality* and *document representation quality*, overcoming the dimensional fragmentation inherent in conventional metrics. It achieves semantic alignment through word-level consistency modeling and carefully engineered, task-specific prompting. Extensive experiments demonstrate that WALM achieves strong agreement with human annotations (Spearman ρ > 0.85) across diverse topic models, significantly outperforming existing metrics while enabling reliable, cross-model comparisons. The implementation, including code and a ready-to-use toolkit, is publicly released.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Topic Model Evaluation
• Comparative Analysis
• Comprehensive Assessment

Innovation

• WALM
• Semantic Quality Evaluation
• Large Language Model Integration