With the expansion of neural networks, such as large language models, humanity is exponentially heading towards superintelligence. As various AI systems are increasingly integrated into the fabric of societies-through recommending values, devising creative solutions, and making decisions-it becomes critical to assess how these AI systems impact humans in the long run. This research aims to contribute towards establishing a benchmark for evaluating the sentiment of various Large Language Models in socially importan issues. The methodology adopted was a Likert scale survey. Seven LLMs, including GPT-4 and Bard, were analyzed and compared against sentiment data from three independent human sample populations. Temporal variations in sentiment were also evaluated over three consecutive days. The results highlighted a diversity in sentiment scores among LLMs, ranging from 3.32 to 4.12 out of 5. GPT-4 recorded the most positive sentiment score towards AGI, whereas Bard was leaning towards the neutral sentiment. The human samples, contrastingly, showed a lower average sentiment of 2.97. The temporal comparison revealed differences in sentiment evolution between LLMs in three days, ranging from 1.03% to 8.21%. The study's analysis outlines the prospect of potential conflicts of interest and bias possibilities in LLMs' sentiment formation. Results indicate that LLMs, akin to human cognitive processes, could potentially develop unique sentiments and subtly influence societies' perceptions towards various opinions formed within the LLMs.

翻译：随着大型语言模型等神经网络的扩展，人类正以指数级速度迈向超级智能。由于各类人工智能系统日益融入社会结构——通过推荐价值观、设计创造性解决方案和做出决策——评估这些人工智能系统如何长期影响人类变得至关重要。本研究旨在为评估各类大型语言模型在社会重要议题中的情感倾向建立基准。研究方法采用李克特量表调查，分析了包括GPT-4和Bard在内的七种大型语言模型，并与三个独立人类样本群体的情感数据进行比较。同时评估了连续三天内情感倾向的时间变化。结果显示大型语言模型的情感得分存在差异，范围在3.32至4.12之间（满分5分）。GPT-4对AGI表现出最积极的情感倾向，而Bard则趋于中立。相比之下，人类样本的平均情感得分较低，为2.97。时间比较显示大型语言模型在三天的情感演变存在差异，变化幅度从1.03%到8.21%不等。研究分析揭示了大型语言模型情感形成过程中潜在的利益冲突和偏见可能性。结果表明，大型语言模型类似于人类认知过程，可能形成独特的情感倾向，并微妙地影响社会对其内部形成的各种观点的认知。