小模型经提示工程可媲美大模型，高效识别可持续发展目标
A Comparative Study of Task Adaptation Techniques of Large Language Models for Identifying Sustainable Development Goals
- 用提示工程优化小模型完成可持续发展目标分类
- 小模型在提示工程下表现接近GPT等大模型
- 适合资源有限但需快速部署的可持续发展分析场景
2012年联合国提出17项可持续发展目标（SDGs），旨在2030年前构建更可持续的未来。然而，由于数据规模庞大且复杂，追踪进展极为困难。文本分类模型成为关键工具，可自动化分析多源文本。近年来，大语言模型（LLMs）凭借其对复杂语义和语言模式的识别能力，在自然语言处理任务中表现突出，包括文本分类。本研究针对单标签多分类任务，评估了多种专有及开源LLMs在识别SDGs方面的表现，并比较了零样本学习（Zero-Shot）、少样本学习（Few-Shot）与微调（Fine-Tuning）等任务适配技术的效果。结果显示，经过提示工程优化的小模型，性能可与OpenAI的GPT等大型模型相媲美。
原文摘要 · Abstract (English)
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).
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