LLMs quantify internal narratives to map depressive states

Researchers have developed a method to quantify depressive states by analyzing participants' internal narratives using large language models. Across two studies involving over 1200 participants, they found that verbal descriptions of symptoms contained granular information predictive of depression scores. The study also demonstrated that changes in these quantified narratives could lead to subsequent changes in self-reported affective states, suggesting a computational approach to understanding and potentially treating psychological conditions. AI

IMPACT This research offers a novel computational approach to understanding and potentially treating psychological conditions by leveraging LLMs to analyze internal narratives.

RANK_REASON The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Jakub Onysk
• Large Language Models

• paper
• safety

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