Your LLM is probably overkill for text classification (jeffyclassify.com)

The article highlights a growing concern within the AI/ML community regarding the overuse of large language models (LLMs) for text classification tasks such as support ticket routing and intent detection. Many developers resort to LLMs because of their initial effectiveness, only to later face issues of high latency, elevated costs, and complications with output formatting. The author suggests a more efficient approach: using frozen sentence embedding models paired with a logistic regression classifier. This method not only simplifies the process by removing the need for fine-tuning and GPU resources but also offers superior classification results. Using the Banking77 dataset for customer intent classification with 77 nuanced labels, the author demonstrates that the embedding model achieves nearly 94% accuracy at a latency of 118 milliseconds, while various LLMs lag far behind, with accuracies below 70% and significantly higher processing times. This disparity underscores the limitations of LLMs in high-classification scenarios where subtle differences are critical. By opting for embedding models, developers can reduce costs and improve efficiency while generating structured outputs, as highlighted by recent research illustrating the effectiveness of embedding models in classification tasks.