AI simulates patient self-stigma with internal monologue dialogues

Researchers have developed a new framework called Stigmatized Self-Reflection (SSR) to better simulate patient self-stigma in large language models. This approach incorporates internal monologues into mental health dialogues, allowing AI agents to exhibit more realistic context-sensitive resistance behaviors like avoidance or self-blame. By fine-tuning LLMs with a specialized dataset and using a chain-of-thought method, the SSR framework enables patient agents to dynamically adjust their expression of stigma, leading to more authentic responses for clinical training and empathetic dialogue systems. AI

IMPACT Enhances realism in AI-driven mental health training simulations by modeling nuanced self-stigma.

RANK_REASON The cluster contains an academic paper detailing a novel framework and dataset for simulating a specific psychological phenomenon in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• large language models (LLMs)
• Stigmatized Self-Reflection (SSR)

• paper
• safety

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