Utilizing large language models to identify evidence of suicidality risk through analysis of emotionally charged posts
Proceedings of the 9th Workshop on Computational Linguistics and …, 2024•aclanthology.org
This paper presents our contribution to the CLPsych 2024 shared task, focusing on the use
of open-source large language models (LLMs) for suicide risk assessment through the
analysis of social media posts. We achieved first place (out of 15 participating teams) in the
task of providing summarized evidence of a user's suicide risk. Our approach is based on
Retrieval Augmented Generation (RAG), where we retrieve the top-k (k= 5) posts with the
highest emotional charge and provide the level of three different negative emotions …
of open-source large language models (LLMs) for suicide risk assessment through the
analysis of social media posts. We achieved first place (out of 15 participating teams) in the
task of providing summarized evidence of a user's suicide risk. Our approach is based on
Retrieval Augmented Generation (RAG), where we retrieve the top-k (k= 5) posts with the
highest emotional charge and provide the level of three different negative emotions …
Abstract
This paper presents our contribution to the CLPsych 2024 shared task, focusing on the use of open-source large language models (LLMs) for suicide risk assessment through the analysis of social media posts. We achieved first place (out of 15 participating teams) in the task of providing summarized evidence of a user’s suicide risk. Our approach is based on Retrieval Augmented Generation (RAG), where we retrieve the top-k (k= 5) posts with the highest emotional charge and provide the level of three different negative emotions (sadness, fear, anger) for each post during the generation phase.
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