The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks. In this era of open-ended language modeling, where task boundaries are gradually fading, an urgent question emerges: have we made significant advances in text classification under the full benefit of LLMs? To answer this question, we propose RGPT, an adaptive boosting framework tailored to produce a specialized text classification LLM by recurrently ensembling a pool of strong base learners. The base learners are constructed by adaptively adjusting the distribution of training samples and iteratively fine-tuning LLMs with them. Such base learners are then ensembled to be a specialized text classification LLM, by recurrently incorporating the historical predictions from the previous learners. Through a comprehensive empirical comparison, we show that RGPT significantly outperforms 8 SOTA PLMs and 7 SOTA LLMs on four benchmarks by 1.36% on average. Further evaluation experiments show a clear surpassing of RGPT over human classification.

翻译：文本分类未来研究的价值因大语言模型在众多下游自然语言处理任务中展现出的卓越效能而面临挑战与不确定性。在这个任务边界逐渐模糊的开放语言建模时代，一个紧迫问题浮现：在大语言模型的全方位助力下，我们在文本分类领域是否已取得显著进展？为解答此问题，我们提出RGPT，一种自适应增强框架，通过循环集成一组强基础学习器，专门生成针对文本分类的大语言模型。基础学习器通过自适应调整训练样本分布并迭代微调大语言模型来构建，随后通过循环整合前序学习器的历史预测结果，将这些基础学习器集成成专门的文本分类大语言模型。通过全面的实证比较，我们证明RGPT在四个基准测试中平均以1.36%的幅度显著优于8个最先进的预训练语言模型和7个最先进的大语言模型。进一步的评估实验表明，RGPT在分类表现上明确超越了人类水平。