Abstract
In 2012, the United Nations introduced 17 Sustainable Development Goals (SDGs) aimed at creating a more sustainable and improved future by 2030. However, tracking progress toward these goals is difficult because of the extensive scale and complexity of the data involved. Text classification models have become vital tools in this area, automating the analysis of vast amounts of text from a variety of sources. Additionally, large language models (LLMs) have recently proven indispensable for many natural language processing tasks, including text classification, thanks to their ability to recognize complex linguistic patterns and semantics. This study analyzes various proprietary and open-source LLMs for a single-label, multi-class text classification task focused on the SDGs. Then, it also evaluates the effectiveness of task adaptation techniques (i.e., in-context learning approaches), namely Zero-Shot and Few-Shot Learning, as well as Fine-Tuning within this domain. The results reveal that smaller models, when optimized through prompt engineering, can perform on par with larger models like OpenAI's GPT (Generative Pre-trained Transformer).
Abstract (translated)
2012年，联合国推出了17项可持续发展目标（SDGs），旨在通过到2030年的努力创造一个更加可持续和改善的未来。然而，由于涉及的数据规模庞大且复杂，追踪这些目标的进展情况变得十分困难。文本分类模型在这一领域中成为了至关重要的工具，能够自动分析来自各种来源的大批量文本数据。此外，大型语言模型（LLMs）最近被证明对于许多自然语言处理任务来说是不可或缺的，包括文本分类，这是因为它们具备识别复杂语言模式和语义的能力。本研究针对SDGs的一项单一标签、多类别文本分类任务，评估了多种专有及开源的大规模语言模型的性能。此外，该研究还评估了在这一领域中任务适应技术（即上下文学习方法）的有效性，具体包括零样本学习和少量样本学习，以及微调的方法。结果表明，在通过提示工程进行优化后，较小的语言模型可以与像OpenAI的GPT（生成式预训练变换器）这样的大型语言模型一样高效地工作。
URL
https://arxiv.org/abs/2506.15208
