A comparative study of transformer-based embeddings for topic coherence

A research study comparing seven transformer-based language models of varying sizes (22M to 13B parameters) in topic modeling tasks found that model size has negligible impact on topic quality. This suggests smaller, more efficient models can match larger models' performance for topic coherence applications, potentially reducing computational costs without sacrificing output quality.

This comparative study addresses a fundamental assumption in modern NLP: that bigger language models necessarily produce better results. By systematically evaluating transformer architectures ranging from MiniLM to LLaMA-2 within a BERTopic pipeline, researchers demonstrate that topic coherence—a critical metric for text organization and understanding—plateaus across model scales. The research applies established evaluation frameworks from Röder et al. (2015), lending methodological rigor to the findings.

The implications extend beyond academic interest into practical deployment scenarios. Organizations implementing topic modeling for document classification, content recommendation, or knowledge extraction have traditionally assumed they needed the largest available models to achieve competitive results. This study challenges that premise. The negligible performance differences across a 590x parameter range suggest an efficiency frontier exists well below cutting-edge model sizes, with smaller architectures delivering equivalent topic quality at substantially lower computational and infrastructure costs.

For developers and enterprises, this creates meaningful optimization opportunities. Deploying MiniLM or comparable smaller models reduces inference latency, memory requirements, and energy consumption while maintaining topic coherence standards. This efficiency gain matters particularly for edge deployment, real-time applications, and resource-constrained environments. The research validates a scaling law boundary for topic modeling tasks specifically, distinguishing it from domains where scale continues driving measurable improvements.

Future work should examine whether these findings generalize to other NLP downstream tasks and explore the specific parameter thresholds where topic quality plateaus. Understanding whether this pattern reflects fundamental task characteristics or BERTopic pipeline specifics would help practitioners make data-driven model selection decisions.

• →Model size from 22M to 13B parameters shows negligible impact on topic coherence quality in transformer-based embeddings
• →Smaller models like MiniLM achieve comparable topic modeling performance to larger models like LLaMA-2
• →Study validates cost-efficiency gains by using smaller models without sacrificing topic quality metrics
• →Findings suggest an efficiency plateau exists for topic modeling tasks below current model scaling trends
• →Results enable practical optimization for resource-constrained deployments while maintaining coherence standards