@inproceedings{shum-etal-2026-measuring,
title = "Measuring the Impact of Big Five Personality Traits on Suicide Severity Level Detection with Large Language Models",
author = "Shum, Kit May and
Sakai, Yusuke and
Kaneko, Masahiro and
Watanabe, Taro",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.71/",
pages = "1392--1411",
ISBN = "979-8-89176-444-6",
abstract = "Sensitive content warning: This paper contains sensitive content related to suicide.\par Suicide is one of the leading causes of global mortality, making risk detection a critical public health priority. Although psychological studies have identified a link between personality and suicide, this relationship has received little attention and has not yet been systematically examined or applied in the context of large language models (LLMs). In this study, we investigate whether LLMs can capture and leverage this relationship for suicide severity detection. We propose incorporating persona profiles based on the Big Five personality traits to simulate user characteristics in a zero-shot setting, enabling us to assess the impact of personality on detection performance. Experimental results show that Neuroticism yields a modest but consistent improvement across most models, with statistically significant gains in several cases. These findings suggest that LLMs can capture inter-domain relationships, and that further refinement of personality-aware modelling may lead to performance improvements."
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<abstract>Sensitive content warning: This paper contains sensitive content related to suicide.\par Suicide is one of the leading causes of global mortality, making risk detection a critical public health priority. Although psychological studies have identified a link between personality and suicide, this relationship has received little attention and has not yet been systematically examined or applied in the context of large language models (LLMs). In this study, we investigate whether LLMs can capture and leverage this relationship for suicide severity detection. We propose incorporating persona profiles based on the Big Five personality traits to simulate user characteristics in a zero-shot setting, enabling us to assess the impact of personality on detection performance. Experimental results show that Neuroticism yields a modest but consistent improvement across most models, with statistically significant gains in several cases. These findings suggest that LLMs can capture inter-domain relationships, and that further refinement of personality-aware modelling may lead to performance improvements.</abstract>
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%0 Conference Proceedings
%T Measuring the Impact of Big Five Personality Traits on Suicide Severity Level Detection with Large Language Models
%A Shum, Kit May
%A Sakai, Yusuke
%A Kaneko, Masahiro
%A Watanabe, Taro
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F shum-etal-2026-measuring
%X Sensitive content warning: This paper contains sensitive content related to suicide.\par Suicide is one of the leading causes of global mortality, making risk detection a critical public health priority. Although psychological studies have identified a link between personality and suicide, this relationship has received little attention and has not yet been systematically examined or applied in the context of large language models (LLMs). In this study, we investigate whether LLMs can capture and leverage this relationship for suicide severity detection. We propose incorporating persona profiles based on the Big Five personality traits to simulate user characteristics in a zero-shot setting, enabling us to assess the impact of personality on detection performance. Experimental results show that Neuroticism yields a modest but consistent improvement across most models, with statistically significant gains in several cases. These findings suggest that LLMs can capture inter-domain relationships, and that further refinement of personality-aware modelling may lead to performance improvements.
%U https://aclanthology.org/2026.findings-aacl.71/
%P 1392-1411
Markdown (Informal)
[Measuring the Impact of Big Five Personality Traits on Suicide Severity Level Detection with Large Language Models](https://aclanthology.org/2026.findings-aacl.71/) (Shum et al., Findings 2026)
ACL