Title: Towards New Benchmark for AI Alignment & Sentiment Analysis in Socially Important Issues: A Comparative Study of Human and LLMs in the Context of AGI Show Chinese title

Title: 面向社会重要问题中人工智能对齐与情感分析的新基准：AGI背景下人类与LLMs的比较研究

Abstract: 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. Show Chinese abstract

Abstract: 随着神经网络的扩展，如大型语言模型，人类正以指数级的速度迈向超级智能。 随着各种人工智能系统越来越多地融入社会结构——通过推荐价值观、制定创造性解决方案和做出决策——评估这些人工智能系统在长期内对人类的影响变得至关重要。 本研究旨在为评估大型语言模型在社会重要问题上的情感提供一个基准。 采用的方法是一种李克特量表调查。 分析并比较了七种大型语言模型，包括GPT-4和Bard，以及来自三个独立人类样本群体的情感数据。 还评估了三天内情感的时空变化。 结果突出显示了大型语言模型之间情感评分的多样性，范围从5分中的3.32到4.12。 GPT-4在AGI方面记录了最积极的情感评分，而Bard则倾向于中性情感。 相比之下，人类样本的平均情感较低，为2.97。 时间比较显示，在三天内大型语言模型的情感演变存在差异，范围从1.03%到8.21%。 该研究的分析概述了大型语言模型情感形成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，可能会发展出独特的情感，并微妙地影响社会对大型语言模型内形成的各类观点的感知。