Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Models

2025-09-08

AI Summary

Existing automatic evaluation metrics (e.g., coherence, diversity) capture only shallow statistical patterns and fail to detect semantic failures or dynamic topic drift. To address this, we propose the first LLM-driven automated evaluation framework specifically designed for dynamic topic models, targeting knowledge organization scenarios such as digital libraries. Our framework introduces nine interpretable metrics across four dimensions: semantic plausibility, structural consistency, document alignment, and evolutionary stability. Innovatively adopting a *use-case-oriented* evaluation paradigm, it integrates adversarial testing and sampling-based validation protocols. We systematically evaluate diverse topic models and open-source LLMs on news, academic, and social media corpora. Experiments demonstrate that our framework significantly enhances semantic sensitivity and robustness in assessment, accurately identifying topic-level defects—including redundancy and drift—that conventional metrics overlook. It establishes a scalable, interpretable, and operationally grounded evaluation paradigm for monitoring topic quality in dynamic environments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

This study presents a framework for automated evaluation of dynamically evolving topic models using Large Language Models (LLMs). Topic modeling is essential for organizing and retrieving scholarly content in digital library systems, helping users navigate complex and evolving knowledge domains. However, widely used automated metrics, such as coherence and diversity, often capture only narrow statistical patterns and fail to explain semantic failures in practice. We introduce a purpose-oriented evaluation framework that employs nine LLM-based metrics spanning four key dimensions of topic quality: lexical validity, intra-topic semantic soundness, inter-topic structural soundness, and document-topic alignment soundness. The framework is validated through adversarial and sampling-based protocols, and is applied across datasets spanning news articles, scholarly publications, and social media posts, as well as multiple topic modeling methods and open-source LLMs. Our analysis shows that LLM-based metrics provide interpretable, robust, and task-relevant assessments, uncovering critical weaknesses in topic models such as redundancy and semantic drift, which are often missed by traditional metrics. These results support the development of scalable, fine-grained evaluation tools for maintaining topic relevance in dynamic datasets. All code and data supporting this work are accessible at https://github.com/zhiyintan/topic-model-LLMjudgment.

Problem

• Automated evaluation of dynamic topic models
• Addressing limitations of traditional coherence metrics
• Assessing semantic quality across multiple dimensions

Innovation

• LLM-based metrics for topic quality
• Framework spans four evaluation dimensions
• Adversarial and sampling-based validation protocols