A comparative study of transformer-based embeddings for topic coherence

• URL: http://arxiv.org/abs/2605.28832v1
• Date: Fri, 10 Apr 2026 08:34:47 GMT
• Title: A comparative study of transformer-based embeddings for topic coherence
• Abstract summary: We study the effect of model size on topic quality by analyzing the performances of seven transformer-based language models.<n>Our results indicate that model size, ranging from 22 million to 13 billion parameters, has a negligible impact on the quality of the topic.
• Score: 0.43748379918040853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Topic modeling is a branch of Natural Language Processing (NLP) that aims to organize large collections of texts into coherent groups according to word co-occurrence patterns, with Latent Dirichlet Allocation (LDA) remaining one of the most widely used and interpretable probabilistic approaches. Recent advances in NLP, particularly transformer-based language models, offer improved document representations. It is also known that the size of the model (in terms of number of parameters) has a significant impact in the performance of the language models on different pre-defined tasks. In this study, we systematically examine the effect of model size on topic quality by analyzing the performances of seven transformer-based language models (from small models such as MiniLM to large ones such as LLaMA-2) in a BERTopic pipeline on a variety of corpora. Topic quality is evaluated using coherence and divergence metrics following R{ö}der et al. (2015). Our results indicate that model size, ranging from 22 million to 13 billion parameters, has a negligible impact on the quality of the topic, suggesting that smaller models can achieve comparable performance to larger models.

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