This paper examines the comparative effectiveness of a specialized compiled language model and a general-purpose model like OpenAI's GPT-3.5 in detecting SDGs within text data. It presents a critical review of Large Language Models (LLMs), addressing challenges related to bias and sensitivity. The necessity of specialized training for precise, unbiased analysis is underlined. A case study using a company descriptions dataset offers insight into the differences between the GPT-3.5 and the specialized SDG detection model. While GPT-3.5 boasts broader coverage, it may identify SDGs with limited relevance to the companies' activities. In contrast, the specialized model zeroes in on highly pertinent SDGs. The importance of thoughtful model selection is emphasized, taking into account task requirements, cost, complexity, and transparency. Despite the versatility of LLMs, the use of specialized models is suggested for tasks demanding precision and accuracy. The study concludes by encouraging further research to find a balance between the capabilities of LLMs and the need for domain-specific expertise and interpretability.

翻译：本文考察了专用编译语言模型与OpenAI的GPT-3.5等通用模型在文本数据中检测可持续发展目标的比较效能。研究对大型语言模型（LLMs）进行了批判性评述，探讨了与偏见和敏感性相关的挑战，并强调了为获得精准、无偏分析而进行专门训练的必要性。通过企业描述数据集的案例研究，揭示了GPT-3.5与专用SDG检测模型之间的差异：GPT-3.5虽覆盖范围更广，但可能识别出与企业活动关联度较低的SDG；而专用模型则聚焦于高度相关的SDG。研究强调，应综合考虑任务需求、成本、复杂性和透明度，审慎选择模型。尽管LLMs具有通用性，但在需要精确性和准确性的任务中，建议采用专用模型。本研究最后呼吁进一步探索如何在LLMs能力与领域专长及可解释性需求之间取得平衡。