Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei
arXiv:2601.05004v2 Announce Type: replace Abstract: Self-destructive behaviors are linked to complex psychological states and can be challenging to diagnose. These behaviors may be even harder to identify within subcultural groups due to their unique expressions. As large language models (LLMs) being deployed across various fields, some researchers have begun exploring their application for detecting self-destructive behaviors. Motivated by this, we investigate self-destructive behavior detection within subcultures using current LLM-based methods. However, these methods have two main challenge
The increasing deployment of LLMs across various fields, coupled with emerging research into their application for complex psychological states, makes this a timely investigation.
This research explores the nuanced application of LLMs in detecting challenging psychological states within specific cultural contexts, pushing the boundaries of AI's societal integration and ethical considerations.
The focus on semantic discrepancy and subcultural expressions highlights a new front for LLM application, moving beyond general language understanding to culturally sensitive psychological analysis.
- · AI researchers
- · Mental health tech startups
- · Social science researchers
- · Psychology AI developers
- · Traditional diagnostic methods (potentially)
- · Subcultures resistant to external analysis
- · AI models lacking cultural nuance
LLMs demonstrate potential for specialized psychological analysis beyond general mental health support.
This could lead to the development of AI tools specifically designed for culturally sensitive mental health diagnosis and intervention.
The ethical and privacy implications of AI analyzing highly personal and subcultural expressions will become a significant societal discussion point.
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