用大模型自动学习数据并增强预测，实现高精度可解释分类
LML-DAP: Language Model Learning a Dataset for Data-Augmented Prediction
- 大模型自主总结数据特征，替代传统人工调参
- 测试准确率超90%，复杂数据下表现优于传统机器学习
- 适合需要可解释性与上下文理解的决策场景
分类任务通常依赖机器学习模型，但这类模型在准确性与可解释性之间难以平衡。本文提出一种基于大语言模型（LLM）的新方法，通过‘语言模型学习’（LML）与‘数据增强预测’（DAP）实现可解释分类。不同于传统方法对数据清洗和特征工程的依赖，该方法让大模型像人类一样主动探索数据以判断分类。LML过程对数据集进行摘要与评估，识别出导致各类标签的关键特征；DAP则利用数据摘要与测试样本生成查询，从数据集中检索相关行作为上下文支持分类。实验表明，系统在部分测试案例中准确率超过90%，验证了其在复杂数据下超越传统模型的潜力。代码已开源：https://github.com/Pro-GenAI/LML-DAP。
原文摘要 · Abstract (English)
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
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