A common use of NLP is to facilitate the understanding of large document collections, with models based on Large Language Models (LLMs) replacing probabilistic topic models. Yet the effectiveness of LLM-based approaches in real-world applications remains under explored. This study measures the knowledge users acquire with topic models—including traditional, unsupervised and supervised LLM- based approaches—on two datasets. While LLM-based methods generate more human- readable topics and show higher average win probabilities than traditional models for data exploration, they produce overly generic topics for domain-specific datasets that do not easily allow users to learn much about the documents. Adding human supervision to LLM-based topic models improves data exploration by addressing hallucination and genericity but requires more human efforts. In contrast, traditional models like Latent Dirichlet Allocation (LDA) remain effective for exploration but are less user-friendly. This paper provides best practices—there is no one right model, the choice of models is situation-specific—and suggests potential improvements for scalable LLM- based topic models.

Topic models and document-clustering evaluations either use automated metrics that align poorly with human preferences, or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners’ real-world usage of models. Annotators—or an LLM-based proxy—review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this protocol, we collect extensive crowdworker annotations of outputs from a diverse set of topic models on two datasets. We then use these annotations to validate automated proxies, finding that the best LLM proxy is statistically indistinguishable from a human annotator and can therefore serve as a reasonable substitute in automated evaluations.

In recent years, there has been growing interest in using NLP tools for decision support systems, particularly in Science, Technology, and Innovation (STI). Among these, topic modeling has been widely used for analyzing large document collections, such as scientific articles, research projects, or patents, yet its integration into decision-making systems remains limited. This paper introduces CASE, a tool for exploiting topic information for semantic analysis of large corpora. The core of CASE is a Solr engine with a customized indexing strategy to represent information from Bayesian and Neural topic models that allow efficient topic-enriched searches. Through ad-hoc plug-ins, CASE enables topic inference on new texts and semantic search. We demonstrate the versatility and scalability of CASE through two use cases: the calculation of aggregated STI indicators and the implementation of a web service to help evaluate research projects.

Topic Modeling is a commonly used technique for analyzing unstructured data in various fields, but achieving accurate results and useful models can be challenging, especially for domain experts who lack the knowledge needed to optimize the parameters required by this natural language processing technique. From this perspective, we introduce an Interactive Topic Model Trainer (ITMT) developed within the EU-funded project IntelComp. ITMT is a user-in-the-loop topic modeling tool presented with a graphical user interface that allows the training and curation of different state-of-the-art topic extraction libraries, including some recent neural-based methods, oriented toward the usage by domain experts. This paper reviews ITMT’s functionalities and key implementation aspects in this paper, including a comparison with other tools for topic modeling analysis.