The paper "Pushing The Limit of LLM Capacity for Text Classification" by Yazhou Zhang et al. explores the potential of large language models (LLMs) in text classification tasks. The authors propose RGPT, an adaptive boosting framework designed to create a specialized text classification LLM by recurrently ensembling strong base learners. These base learners are constructed by adjusting the distribution of training samples and iteratively fine-tuning LLMs. The ensemble of these base learners is then used to form a specialized text classification LLM, with each learner incorporating historical predictions from previous learners. The study evaluates RGPT on four benchmark datasets and compares it against 8 state-of-the-art pre-trained language models (PLMs) and 7 state-of-the-art LLMs. RGPT consistently outperforms these models, achieving an average improvement of 1.36% across the datasets. Human evaluation further demonstrates that RGPT surpasses average human classification performance. Key contributions of the paper include: 1. Exploring the value of text classification in the era of LLMs. 2. Proposing RGPT, an adaptive boosting framework to enhance LLMs' classification capabilities. 3. Demonstrating the effectiveness of RGPT through comprehensive experiments on four benchmark datasets. The paper also discusses the limitations of RGPT, such as high computational cost and the need for further testing on a wider range of tasks. Overall, the study provides strong evidence that RGPT can significantly improve the performance of LLMs in text classification tasks.The paper "Pushing The Limit of LLM Capacity for Text Classification" by Yazhou Zhang et al. explores the potential of large language models (LLMs) in text classification tasks. The authors propose RGPT, an adaptive boosting framework designed to create a specialized text classification LLM by recurrently ensembling strong base learners. These base learners are constructed by adjusting the distribution of training samples and iteratively fine-tuning LLMs. The ensemble of these base learners is then used to form a specialized text classification LLM, with each learner incorporating historical predictions from previous learners. The study evaluates RGPT on four benchmark datasets and compares it against 8 state-of-the-art pre-trained language models (PLMs) and 7 state-of-the-art LLMs. RGPT consistently outperforms these models, achieving an average improvement of 1.36% across the datasets. Human evaluation further demonstrates that RGPT surpasses average human classification performance. Key contributions of the paper include: 1. Exploring the value of text classification in the era of LLMs. 2. Proposing RGPT, an adaptive boosting framework to enhance LLMs' classification capabilities. 3. Demonstrating the effectiveness of RGPT through comprehensive experiments on four benchmark datasets. The paper also discusses the limitations of RGPT, such as high computational cost and the need for further testing on a wider range of tasks. Overall, the study provides strong evidence that RGPT can significantly improve the performance of LLMs in text classification tasks.