2023/01/17 by Shi Zong, Zong, Shi, Josh Seltzer +7
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Text and Document Classification Technologies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2301.07006
openalex publication_date 2023/01/17 · openalex created_date 2023/01/20 · openalex updated_date 2026/07/28
Industry practitioners always face the problem of choosing the appropriate model for deployment under different considerations, such as to maximize a metric that is crucial for production, or to reduce the total cost given financial concerns. In this work, we focus on the text classification task and present a quantitative analysis for this challenge. Using classification accuracy as the main metric, we evaluate the classifiers' performances for a variety of models, including large language models, along with their associated costs, including the annotation cost, training (fine-tuning) cost, and inference cost. We then discuss the model choices for situations like having a large number of samples needed for inference. We hope our work will help people better understand the cost/quality trade-offs for the text classification task.