Feb. 13, 2024, 5:49 a.m. | Yazhou Zhang Mengyao Wang Chenyu Ren Qiuchi Li Prayag Tiwari Benyou Wang Jing Qin

cs.CL updates on arXiv.org arxiv.org

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 …

advances capacity challenges classification cs.cl future language language models large language large language models llm llms modeling nlp question research tasks text text classification value

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