Large Language Models for Detection of Life-Threatening Texts

TL;DR

This study evaluates large language models for detecting life-threatening texts, showing they outperform traditional methods across various data imbalance scenarios, highlighting their potential for real-world applications.

Contribution

It introduces fine-tuning of open-source LLMs for life-threatening text detection and compares their performance with traditional NLP techniques.

Findings

LLMs outperform traditional methods in detecting life-threatening texts.

Mistral and Llama-2 are top performers across data scenarios.

Upsampling benefits traditional methods more than LLMs.

Abstract

Detecting life-threatening language is essential for safeguarding individuals in distress, promoting mental health and well-being, and preventing potential harm and loss of life. This paper presents an effective approach to identifying life-threatening texts using large language models (LLMs) and compares them with traditional methods such as bag of words, word embedding, topic modeling, and Bidirectional Encoder Representations from Transformers. We fine-tune three open-source LLMs including Gemma, Mistral, and Llama-2 using their 7B parameter variants on different datasets, which are constructed with class balance, imbalance, and extreme imbalance scenarios. Experimental results demonstrate a strong performance of LLMs against traditional methods. More specifically, Mistral and Llama-2 models are top performers in both balanced and imbalanced data scenarios while Gemma is slightly behind. We employ the upsampling technique to deal with the imbalanced data scenarios and demonstrate that while this method benefits traditional approaches, it does not have as much impact on LLMs. This study demonstrates a great potential of LLMs for real-world life-threatening language detection problems.