Large Language Model Enhanced Machine Learning Estimators for Classification
This work addresses classification performance enhancement for machine learning practitioners, though it appears incremental as it combines existing LLMs with classical methods.
The authors tackled classification problems by integrating large language models (LLMs) into classical supervised machine learning estimators, resulting in significant prediction performance improvements across four datasets in both standard and transfer learning tasks.
Pre-trained large language models (LLM) have emerged as a powerful tool for simulating various scenarios and generating output given specific instructions and multimodal input. In this work, we analyze the specific use of LLM to enhance a classical supervised machine learning method for classification problems. We propose a few approaches to integrate LLM into a classical machine learning estimator to further enhance the prediction performance. We examine the performance of the proposed approaches through both standard supervised learning binary classification tasks, and a transfer learning task where the test data observe distribution changes compared to the training data. Numerical experiments using four publicly available datasets are conducted and suggest that using LLM to enhance classical machine learning estimators can provide significant improvement on prediction performance.