Short Text Classification (STC) is crucial for processing and comprehending
the brief but substantial content prevalent on contemporary digital platforms.
The STC encounters difficulties in grasping semantic and syntactic intricacies,
an issue that is apparent in traditional pre-trained language models. Although
Graph Convolutional Networks enhance performance by integrating external
knowledge bases, these methods are limited by the quality and extent of the
knowledge applied. Recently, the emergence of Large Language Models (LLMs) and
Chain-of-Thought (CoT) has significantly improved the performance of complex
reasoning tasks. However, some studies have highlighted the limitations of
their application in fundamental NLP tasks. Consequently, this study sought to
employ CoT to investigate the capabilities of LLMs in STC tasks. This study
introduces Quartet Logic: A Four-Step Reasoning (QLFR) framework. This
framework primarily incorporates Syntactic and Semantic Enrichment CoT,
effectively decomposing the STC task into four distinct steps: (i) essential
concept identification, (ii) common-sense knowledge retrieval, (iii) text
rewriting, and (iv) classification. This elicits the inherent knowledge and
abilities of LLMs to address the challenges in STC. Surprisingly, we found that
QLFR can also improve the performance of smaller models. Therefore, we
developed a CoT-Driven Multi-task learning (QLFR-CML) method to facilitate the
knowledge transfer from LLMs to smaller models. Extensive experimentation
across six short-text benchmarks validated the efficacy of the proposed
methods. Notably, QLFR achieved state-of-the-art performance on all datasets,
with significant improvements, particularly on the Ohsumed and TagMyNews
datasets.

研究引入了 Quartet Logic：A Four-Step Reasoning (QLFR) 框架，通过结合链式思考和大型语言模型，将短文本分类任务分解为四个步骤，取得了显著的性能提升，并发现这个框架也可以改进较小型模型的性能。