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Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
April 23, 2024, 4:43 a.m. | Qingyang Wu, Ying Xu, Tingsong Xiao, Yunze Xiao, Yitong Li, Tianyang Wang, Yichi Zhang, Shanghai Zhong, Yuwei Zhang, Wei Lu, Yifan Yang
cs.LG updates on arXiv.org arxiv.org
Abstract: Large Language Models (LLMs) have emerged as potent tools for advancing the United Nations' Sustainable Development Goals (SDGs). However, the attitudinal disparities between LLMs and humans towards these goals can pose significant challenges. This study conducts a comprehensive review and analysis of the existing literature on the attitudes of LLMs towards the 17 SDGs, emphasizing the comparison between their attitudes and support for each goal and those of humans. We examine the potential disparities, primarily …
abstract alignment analysis and analysis arxiv challenges cs.ai cs.cl cs.cy cs.lg development however humans language language models large language large language models llms review study sustainable sustainable development tools type united united nations
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