Summary of Building Trust in Mental Health Chatbots: Safety Metrics and Llm-based Evaluation Tools, by Jung in Park et al.
Building Trust in Mental Health Chatbots: Safety Metrics and LLM-Based Evaluation Tools
by Jung In Park, Mahyar Abbasian, Iman Azimi, Dawn T. Bounds, Angela Jun, Jaesu Han, Robert M. McCarron, Jessica Borelli, Parmida Safavi, Sanaz Mirbaha, Jia Li, Mona Mahmoudi, Carmen Wiedenhoeft, Amir M. Rahmani
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study aims to develop a framework for evaluating the safety and reliability of mental health chatbots, which are becoming increasingly popular due to their accessibility and human-like interactions. The authors created an evaluation framework with 100 benchmark questions and five guideline questions, validated by mental health experts, and tested it on a GPT-3.5-turbo-based chatbot. The results showed that the agentic approach, which dynamically accesses reliable information, demonstrated the best alignment with human assessments, highlighting the importance of guidelines and ground truth for improving large language model (LLM) evaluation accuracy. The study also emphasized the need for comprehensive, expert-tailored safety evaluation metrics for mental health chatbots. The superior performance of the agentic approach underscores the importance of real-time data access in enhancing chatbot reliability. The authors concluded that their framework is effective in improving safety and reliability, and future work should extend evaluations to accuracy, bias, empathy, and privacy to ensure holistic assessment and responsible integration into healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study wants to make sure that mental health chatbots are safe and reliable for people who need help. Chatbots can talk like humans and are easy to use on phones and computers. The researchers made a special set of questions to test how good these chatbots are at helping people with mental health issues. They used a really smart computer program called GPT-3.5-turbo as an example, and the results showed that this approach was best for making sure the chatbot is giving good answers. The study also found that we need to make sure chatbots don’t say things that are not true or hurtful. The researchers want people to trust these chatbots when they need help, so they’re working on a way to make sure they are safe and reliable. |
Keywords
* Artificial intelligence * Alignment * Gpt * Large language model
