Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts
The study tested how well Large Language Models (LLMs) remain consistent when responding to queries based on emotionally charged text generated by themselves. Using three levels of false claims across extreme and moderate emotions, the results showed LLMs perform below average and struggle most with moderate emotional content. This suggests they are vulnerable to false beliefs and may prioritize generating agreeable responses over accurate evaluation.