  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.
