This paper introduces a new approach to using Large Language Models (LLMs) for classification tasks, which are typically handled using Machine Learning (ML) models. Unlike ML models that rely heavily on data cleaning and feature engineering, this method streamlines the process using LLMs. This paper proposes a new concept called "Language Model Learning (LML)" powered by a new method called "Data-Augmented Prediction (DAP)". The classification is performed by LLMs using a method similar to humans manually exploring and understanding the data and deciding classifications using data as a reference. Training data is summarized and evaluated to determine the features that lead to the classification of each label the most. In the process of DAP, the system uses the data summary to automatically create a query, which is used to retrieve relevant rows from the dataset. A classification is generated by the LLM using data summary and relevant rows, ensuring satisfactory accuracy even with complex data. Usage of data summary and similar data in DAP ensures context-aware decision-making. The proposed method uses the words "Act as an Explainable Machine Learning Model" in the prompt to enhance the interpretability of the predictions by allowing users to review the logic behind each prediction. In some test cases, the system scored an accuracy above 90%, proving the effectiveness of the system and its potential to outperform conventional ML models in various scenarios. The code is available at https://github.com/Pro-GenAI/LML-DAP

翻译：本文提出了一种利用大型语言模型（LLM）处理分类任务的新方法，这类任务传统上通常由机器学习（ML）模型完成。与严重依赖数据清洗和特征工程的ML模型不同，本方法利用LLM简化了流程。文章提出了名为“语言模型学习（LML）”的新概念，其由一种称为“数据增强预测（DAP）”的新方法驱动。分类任务由LLM执行，其过程类似于人类手动探索和理解数据，并以数据为参考进行分类决策。训练数据经过总结和评估，以确定最能导致每个标签分类的特征。在DAP过程中，系统利用数据摘要自动生成查询，用于从数据集中检索相关行。LLM结合数据摘要和相关行生成分类结果，即使在复杂数据下也能保证满意的准确率。DAP中对数据摘要和相似数据的使用确保了上下文感知的决策。所提出的方法在提示词中使用“扮演可解释机器学习模型”的表述，通过允许用户审查每个预测背后的逻辑，增强了预测的可解释性。在部分测试案例中，系统取得了超过90%的准确率，证明了该系统的有效性及其在多种场景下超越传统ML模型的潜力。代码发布于 https://github.com/Pro-GenAI/LML-DAP