# Mental Health Assessment for the Chatbots

arXiv: https://arxiv.org/abs/2201.05382 · HTML: https://mlsanity.com/paper/2201.05382/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 19 of 19)

- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation](https://mlsanity.com/paper/2005.00456/) (2020)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [Unsupervised Evaluation of Interactive Dialog with DialoGPT](https://mlsanity.com/paper/2006.12719/) (2020)
- [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://mlsanity.com/paper/2004.08449/) (2020)
- [Towards Controllable Biases in Language Generation](https://mlsanity.com/paper/2005.00268/) (2020)
- [A Comprehensive Assessment of Dialog Evaluation Metrics](https://mlsanity.com/paper/2106.03706/) (2021)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning](https://mlsanity.com/paper/2006.16779/) (2020)
- [Dialogue Response Ranking Training with Large-Scale Human Feedback Data](https://mlsanity.com/paper/2009.06978/) (2020)
- [Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention](https://mlsanity.com/paper/1910.12038/) (2019)
- [Assessing Dialogue Systems with Distribution Distances](https://mlsanity.com/paper/2105.02573/) (2021)
- [Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency](https://mlsanity.com/paper/2106.02228/) (2021)

## Abstract (arXiv)

Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics. For a chatbot which responds to millions of online users including minors, we argue that it should have a healthy mental tendency in order to avoid the negative psychological impact on them. In this paper, we establish several mental health assessment dimensions for chatbots (depression, anxiety, alcohol addiction, empathy) and introduce the questionnaire-based mental health assessment methods. We conduct assessments on some well-known open-domain chatbots and find that there are severe mental health issues for all these chatbots. We consider that it is due to the neglect of the mental health risks during the dataset building and the model training procedures. We expect to attract researchers' attention to the serious mental health problems of chatbots and improve the chatbots' ability in positive emotional interaction.