@inproceedings{gyanendro-singh-etal-2024-extracting,
title = "Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models",
author = "Gyanendro Singh, Loitongbam and
Mao, Junyu and
Mutalik, Rudra and
Middleton, Stuart",
editor = "Yates, Andrew and
Desmet, Bart and
Prud{'}hommeaux, Emily and
Zirikly, Ayah and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ireland, Molly and
Ophir, Yaakov",
booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clpsych-1.20",
pages = "218--226",
abstract = "This paper explores the use of Large Language Models (LLMs) in analyzing social media content for mental health monitoring, specifically focusing on detecting and summarizing evidence of suicidal ideation. We utilized LLMs Mixtral7bx8 and Tulu-2-DPO-70B, applying diverse prompting strategies for effective content extraction and summarization. Our methodology included detailed analysis through Few-shot and Zero-shot learning, evaluating the ability of Chain-of-Thought and Direct prompting strategies. The study achieved notable success in the CLPsych 2024 shared task (ranked top for the evidence extraction task and second for the summarization task), demonstrating the potential of LLMs in mental health interventions and setting a precedent for future research in digital mental health monitoring.",
}
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%0 Conference Proceedings
%T Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models
%A Gyanendro Singh, Loitongbam
%A Mao, Junyu
%A Mutalik, Rudra
%A Middleton, Stuart
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F gyanendro-singh-etal-2024-extracting
%X This paper explores the use of Large Language Models (LLMs) in analyzing social media content for mental health monitoring, specifically focusing on detecting and summarizing evidence of suicidal ideation. We utilized LLMs Mixtral7bx8 and Tulu-2-DPO-70B, applying diverse prompting strategies for effective content extraction and summarization. Our methodology included detailed analysis through Few-shot and Zero-shot learning, evaluating the ability of Chain-of-Thought and Direct prompting strategies. The study achieved notable success in the CLPsych 2024 shared task (ranked top for the evidence extraction task and second for the summarization task), demonstrating the potential of LLMs in mental health interventions and setting a precedent for future research in digital mental health monitoring.
%U https://aclanthology.org/2024.clpsych-1.20
%P 218-226
Markdown (Informal)
[Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models](https://aclanthology.org/2024.clpsych-1.20) (Gyanendro Singh et al., CLPsych-WS 2024)
ACL