Large language models (LLMs) are already being piloted for clinical use in
hospital systems like NYU Langone, Dana-Farber and the NHS. A proposed
deployment use case is psychotherapy, where a LLM-powered chatbot can treat a
patient undergoing a mental health crisis. Deployment of LLMs for mental health
response could hypothetically broaden access to psychotherapy and provide new
possibilities for personalizing care. However, recent high-profile failures,
like damaging dieting advice offered by the Tessa chatbot to patients with
eating disorders, have led to doubt about their reliability in high-stakes and
safety-critical settings.
In this work, we develop an evaluation framework for determining whether LLM
response is a viable and ethical path forward for the automation of mental
health treatment. Using human evaluation with trained clinicians and automatic
quality-of-care metrics grounded in psychology research, we compare the
responses provided by peer-to-peer responders to those provided by a
state-of-the-art LLM.
We show that LLMs like GPT-4 use implicit and explicit cues to infer patient
demographics like race. We then show that there are statistically significant
discrepancies between patient subgroups: Responses to Black posters
consistently have lower empathy than for any other demographic group (2%-13%
lower than the control group). Promisingly, we do find that the manner in which
responses are generated significantly impacts the quality of the response. We
conclude by proposing safety guidelines for the potential deployment of LLMs
for mental health response.

我们开发了一个评估框架，以确定大型语言模型在自动化心理健康治疗方面是否是可行和道德的前进路径，并通过人工评估和心理学研究的自动质量评估指标，比较了点对点响应者提供的回应与一种最先进的大型语言模型提供的回应。我们展示了像 GPT-4 这样的大型语言模型使用隐式和显式线索推断患者人口统计学特征，然后展示了患者子群之间存在统计上显著差异：对于黑人发帖的回应一直比其他人口统计群体的同一回应具有较低的同理心（比对照组低 2%-13%）。我们发现回应生成的方式显著影响回应的质量。最后，我们提出了大型语言模型在心理健康响应潜在部署中的安全指南。

AI 是否能相似地感知：测试大型语言模型对精神健康支持的响应

Can AI Relate: Testing Large Language Model Response for Mental Health  Support

Data-driven methods for mental health treatment and surveillance have become
a major focus in computational science research in the last decade. However,
progress in the domain, in terms of both medical understanding and system
performance, remains bounded by the availability of adequate data. Prior
systematic reviews have not necessarily made it possible to measure the degree
to which data-related challenges have affected research progress. In this
paper, we offer an analysis specifically on the state of social media data that
exists for conducting mental health research. We do so by introducing an
open-source directory of mental health datasets, annotated using a standardized
schema to facilitate meta-analysis.

本论文分析了社交媒体数据对于进行心理健康研究的现状，并引入了一个基于标准化模式的开源心理健康数据集目录，以促进元分析。