When XGBoost Outperforms GPT-4 on Text Classification: A Case Study
Matyáš Boháček, Michal Bravansky · 2024
Large language models (LLMs) are increasingly used for applications beyond text generation, ranging from text summarization to instruction following.One popular example of exploiting LLMs' zero-and few-shot capabilities is the task of text classification.This short paper compares two popular LLM-based classification pipelines (GPT-4 and LLAMA 2) to a popular pre-LLM-era classification pipeline on the task of news trustworthiness classification, focusing on performance, training, and deployment requirements.We find that, in this case, the pre-LLM-era ensemble pipeline outperforms the two popular LLM pipelines while being orders of magnitude smaller in parameter size.