Empathic Dialog Systems for Patient Intake: Balancing Task-Completion and Emotional Support using RAGs
Bibliographic record
Abstract
Dialogue systems are increasingly recognized as valuable assets in healthcare, aiding in the support of individuals grappling with mental health challenges. In this paper, we develop and evaluate a task-oriented empathic chatbot, EmoBot, in a mental healthcare patient intake scenario. EmoBot employs Retrieval-Augmented Generation (RAG) methods to provide guardrails to Large Language Models (LLMs) in providing targeted empathic support, while ensuring task-completion of administering PHQ-9 intake questionnaire. We evaluate EmoBot’s performance by using a simulated patient model to mimic varying levels of depression severity. Our preliminary results showed high task completion rates and agreement between EmoBot’s depression severity categorization and human raters, demonstrating the promise of RAG-based methods in guiding LLMs to be used for health assessment and support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".