Chat, Summary and Diagnosis: A LLM - Enhanced Conversational Agent for Interactive Depression Detection

Depression is a significant mental illness that affects how individuals express their emotions and engage with others, making communication challenging. Most depression assessment tools utilize self-report questionnaires, such as the Patient Health Questionnaire (PHQ-9). These psychometric instruments can be easily adapted to electronic forms. However, this approach cannot provide human-like explanations and interactions, leading to a poor interactivity. Furthermore, we have identified critical limitations in previous prompting methods. They are either constrained to queries using a single identifiable relation, or being agnostic to input contexts, making it difficult to capture variabilities that occur across different inference steps. To solve these issues, we develop a large language models LLM-enhanced conversational agent for depression detection, which makes it more effective and interactive. Specifically, we first explore an iterative knowledge-aware prompter (IKP), a new prompting paradigm which inject specific knowledge from language models progressively for multi-step reasoning, which learns to synthesize prompts conditioned on the current step's contexts. Second, our proposed system introduces a multi-step diagnosis (MSD) approach. Our system not only delivers a diagnosis but also generates a symptom summary through interactive conversations. Our proposed agent enables users to have interactive natural language dialogues with the system, enhancing their personalized comprehension of mental states. Our experiments demonstrate the effectiveness of the iterative knowledge-aware prompter design.