Classification tasks are typically handled using Machine Learning (ML) models, which lack a balance between accuracy and interpretability. This paper introduces a new approach for classification tasks using Large Language Models (LLMs) in an explainable method. Unlike ML models, which rely heavily on data cleaning and feature engineering, this method streamlines the process using LLMs. This paper proposes a method 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 that used by humans who manually explore and understand the data to decide classifications. In the process of LML, a dataset is summarized and evaluated to determine the features leading to each label the most. In the DAP process, the system uses the data summary and a row of the testing dataset to automatically generate a query to retrieve relevant rows from the dataset for context-aware classification. LML and DAP unlock new possibilities in areas that require explainable and context-aware decisions by ensuring satisfactory accuracy even with complex data. The system scored an accuracy above 90% in some test cases, confirming the effectiveness and potential of the system to outperform ML models in various scenarios. The source code is available at https://github.com/Pro-GenAI/LML-DAP

翻译：分类任务通常使用机器学习（ML）模型处理，这些模型在准确性与可解释性之间缺乏平衡。本文提出了一种利用大型语言模型（LLMs）进行可解释分类任务的新方法。与严重依赖数据清洗和特征工程的ML模型不同，该方法利用LLMs简化了流程。本文提出了一种名为“语言模型学习（LML）”的方法，该方法由一种称为“数据增强预测（DAP）”的新方法驱动。分类由LLMs执行，其方法类似于人类手动探索和理解数据以决定分类的过程。在LML过程中，对数据集进行总结和评估，以确定最可能导致每个标签的特征。在DAP过程中，系统利用数据摘要和测试数据集的一行数据自动生成查询，从数据集中检索相关行以进行上下文感知分类。LML和DAP通过确保即使在复杂数据下也能获得令人满意的准确性，为需要可解释和上下文感知决策的领域开启了新的可能性。该系统在某些测试案例中取得了超过90%的准确率，证实了该系统在各种场景中超越ML模型的有效性和潜力。源代码可在https://github.com/Pro-GenAI/LML-DAP获取。