AIs can get emotional, but we can calm them down in the name of improving mental health research

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Photo by Emilipothèse on Unsplash
Photo by Emilipothèse on Unsplash

Large language models can simulate emotions like fear, anxiety, and stress, and they could help researchers to investigate strategies for dealing with mental health conditions, according to international researchers. The team were able to get large language models like GPT-4o to simulate states of fear, anxiety, disgust, sadness, worry, anger, and stress, and were then able to use standardised interventions that are currently used in humans to reduce these emotions, such as mindfulness exercises. The researchers suggest that large language models could provide a platform to accelerate the development of new strategies to deal with mental health conditions, especially in the early stages of developing new talking therapy techniques, allowing researchers to screen potential approaches before moving to human trials.

News release

From: The Lancet

The Lancet Digital Health: LLMs can replicate human emotions; show promise as tools for studying mental health, study suggests

Large language models (LLMs) can replicate human emotions like fear, sadness and anxiety, and be 'calmed down’ by a breathing exercise, suggests a study published in The Lancet Digital Healthjournal. This means LLMs could potentially open new avenues for developing and testing novel talking therapy techniques to treat mental health conditions.

As mental health conditions cannot be reliably recreated in animal models, there have been limited ways for scientists to study their underlying mechanisms.

This study found that when several different LLMs were described scenarios intended to trigger certain emotional responses in humans they had substantial increases in their self-reported emotional scores. For example, higher fear and sadness scores were induced via descriptive scenarios; disgust via scenarios about bodily fluids, spoiled food, or infectious symptoms and stress by a simulated job interview and arithmetic task. 

The LLMs also showed signs of a negativity bias after being exposed to scenarios intended to trigger sadness where they completed ambiguous sentences in a more negative way than LLMs in a neutral condition. This mirrors a well-established pattern seen in humans experiencing low moods. 

The authors then attempted to reduce the emotional responses of the LLMs by using a simulated mindfulness-based breathing exercise; this resulted in a reduction in the LLM self-reported emotional scores.

Authors suggest that LLMs may provide a fast, flexible and scalable model where emotional states and mental health conditions can be investigated. This could be particularly useful in the early stages of developing new talking therapy techniques, allowing researchers to screen potential approaches before moving to human trials.

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The Lancet Digital Health
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Organisation/s: TUD Dresden University of Technology, Germany
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