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).

翻译：2012年，联合国提出了17项可持续发展目标（SDGs），旨在到203年创造一个更可持续、更美好的未来。然而，由于所涉数据的规模庞大且复杂，追踪这些目标的进展十分困难。文本分类模型已成为该领域的重要工具，能够自动化分析来自各种来源的大量文本。此外，大型语言模型（LLMs）凭借其识别复杂语言模式和语义的能力，最近已被证明对包括文本分类在内的许多自然语言处理任务不可或缺。本研究针对以可持续发展目标为中心的单标签多类文本分类任务，分析了多种专有和开源的大型语言模型。随后，还评估了任务适应技术（即上下文学习方法）在该领域的有效性，包括零样本学习、少样本学习以及微调。结果表明，通过提示工程优化后，较小模型的性能可与OpenAI的GPT（生成式预训练Transformer）等较大模型相媲美。