@inproceedings{alhamed-etal-2024-using,
title = "Using Large Language Models ({LLM}s) to Extract Evidence from Pre-Annotated Social Media Data",
author = "Alhamed, Falwah and
Ive, Julia and
Specia, Lucia",
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-1.22",
pages = "232--237",
abstract = "For numerous years, researchers have employed social media data to gain insights into users{'} mental health. Nevertheless, the majority of investigations concentrate on categorizing users into those experiencing depression and those considered healthy, or on detection of suicidal thoughts. In this paper, we aim to extract evidence of a pre-assigned gold label. We used a suicidality dataset containing Reddit posts labeled with the suicide risk level. The task is to use Large Language Models (LLMs) to extract evidence from the post that justifies the given label. We used Meta Llama 7b and lexicons for solving the task and we achieved a precision of 0.96.",
}
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%0 Conference Proceedings
%T Using Large Language Models (LLMs) to Extract Evidence from Pre-Annotated Social Media Data
%A Alhamed, Falwah
%A Ive, Julia
%A Specia, Lucia
%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 alhamed-etal-2024-using
%X For numerous years, researchers have employed social media data to gain insights into users’ mental health. Nevertheless, the majority of investigations concentrate on categorizing users into those experiencing depression and those considered healthy, or on detection of suicidal thoughts. In this paper, we aim to extract evidence of a pre-assigned gold label. We used a suicidality dataset containing Reddit posts labeled with the suicide risk level. The task is to use Large Language Models (LLMs) to extract evidence from the post that justifies the given label. We used Meta Llama 7b and lexicons for solving the task and we achieved a precision of 0.96.
%U https://aclanthology.org/2024.clpsych-1.22
%P 232-237
Markdown (Informal)
[Using Large Language Models (LLMs) to Extract Evidence from Pre-Annotated Social Media Data](https://aclanthology.org/2024.clpsych-1.22) (Alhamed et al., CLPsych-WS 2024)
ACL