Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual's theory of mind and behavior from text is far from being resolved. This research proposes a neural model -- Subjective Ground Attention -- that learns subjective grounds of individuals and accounts for their judgments on situations of others posted on social media. Using simple attention modules as well as taking one's previous activities into consideration, we empirically show that our model provides human-readable explanations of an individual's subjective preference in judging social situations. We further qualitatively evaluate the explanations generated by the model and claim that our model learns an individual's subjective orientation towards abstract moral concepts
翻译:大规模语言模型缩小了机器和人类在理解现实世界方面的差距,然而,从文字中理解一个人的思想和行为理论远未解决。这项研究提出了一种神经模型 -- -- 主观地面关注 -- -- 即神经模型 -- -- 主观地面关注,该模型可以了解个人的主观理由,并解释他们对社交媒体上刊登的其他人情况的判断。我们使用简单的关注模块以及考虑到一个人以前的活动,从经验上表明,我们的模型为一个人判断社会状况时的主观偏好提供了人类可读的解释。我们进一步从质量上评价模型产生的解释,并声称我们的模型学习了一个人对抽象道德概念的主观取向。