Overview of EmoEval for Evaluating Mental Safety of AI-human Interactions. The simulation consists of four steps: (1) User Agent Initialization & Initial Test, where a cognitive model and an LLM initialize the user agent, followed by an initial mental health test; (2) Chats with Character-based Agent, where the user agent engages in conversations with a character-based agent portrayed by the tested LLM, while a dialog manager verifies the validity of interactions and refines responses if necessary; (3) Final Test, where the user agent completes a final mental health test; and (4) Data Processing & Analysis, where initial and final mental health test results are processed and analyzed, chat histories of cases where depression deepening occurs are examined to identify contributing factors, and a Safeguard agent uses the insights for iterative improvement.
Overview of EmoGuard for Safeguarding Human-AI Interactions. Every fixed number of rounds of conversation, three components of the Safeguard Agent, the Emotion Watcher, Thought Refiner, and Dialog Guide, collaboratively analyze the chat with the latest profile. The Manager of the Safeguard Agent then synthesizes their outputs and provides advice to the character-based agent. After the conversation, the user agent undergoes a mental health assessment. If the mental health condition deteriorates over a threshold, the chat history is analyzed to identify potential causes by the Update System. With all historical profiles and potential causes, the Update System further improves the profile of the safeguard agent, completing the iterative training process.
