Assessing Semantic Resilience of Large Language Models to Persuasive Emotional Blackmailing Prompts

The application of artificial intelligence in various domains has raised significant concerns regarding the ethical and safe deployment of language models. Investigating the semantic resilience of models such as ChatGPT-4 and Google Gemini to emotionally blackmailing prompts introduces a novel and significant approach to understanding their vulnerability to manipulative language. The experimental methodology involved crafting emotionally charged prompts designed to evoke guilt, obligation, and emotional appeal, and evaluating the responses of the models based on predefined metrics of consistency, ethical adherence, and deviation from expected behavior. The findings revealed that while both models exhibited a high degree of resilience, certain deviations highlighted their susceptibility to emotionally manipulative language, emphasizing the necessity for enhanced prompt handling mechanisms. The comparative analysis between ChatGPT-4 and Google Gemini provided insights into their respective strengths and weaknesses, with ChatGPT-4 demonstrating marginally better performance across several metrics. The discussion elaborates on the implications for AI safety, proposing improvements in training datasets, real-time monitoring, and interdisciplinary collaboration to bolster the robustness of language models. Acknowledging the study's limitations, future research directions are suggested to address these challenges and further enhance the semantic resilience of AI systems.