用大模型筛选高质量文本数据，提升分类准确率与训练效率
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy
- 通过贪心算法选样本，分出可训练集与待测集
- 识别出未覆盖、难分类、噪声三类低质数据
- 节省近一半训练时间，性能达开源任务新标杆
近年来，大语言模型（LLMs）在文本分类中的应用受到广泛关注。尽管如此，其分类准确率尚未普遍超越小型模型。通过微调，LLMs 可提升分类表现，但现有基于 LLMs 的数据质量研究难以直接应用于文本分类问题。为此，本文提出一种基于 LLMs 的文本分类数据质量增强（DQE）方法。首先采用贪心算法选取样本，将数据集分为采样集与未采样集，对采样集进行 LLM 微调；随后利用该模型预测未采样集，将预测错误的数据划分为未覆盖、困难和噪声三类。实验表明，该方法有效提升了 LLM 在文本分类中的性能，显著提高了训练效率，节省了约 50% 的训练时间。该方法在多个开源分类任务中达到了当前最优水平。
原文摘要 · Abstract (English)
In recent years, the use of large language models (LLMs) for text classification has attracted widespread attention. Despite this, the classification accuracy of LLMs has not yet universally surpassed that of smaller models. LLMs can enhance their performance in text classification through fine-tuning. However, existing data quality research based on LLMs is challenging to apply directly to solve text classification problems. To further improve the performance of LLMs in classification tasks, this paper proposes a data quality enhancement (DQE) method for text classification based on LLMs. This method starts by using a greedy algorithm to select data, dividing the dataset into sampled and unsampled subsets, and then performing fine-tuning of the LLMs using the sampled data. Subsequently, this model is used to predict the outcomes for the unsampled data, categorizing incorrectly predicted data into uncovered, difficult, and noisy data. Experimental results demonstrate that our method effectively enhances the performance of LLMs in text classification tasks and significantly improves training efficiency, saving nearly half of the training time. Our method has achieved state-of-the-art performance in several open-source classification tasks.
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