Summary of Socially Aware Synthetic Data Generation For Suicidal Ideation Detection Using Large Language Models, by Hamideh Ghanadian et al.
Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models
by Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a crucial challenge in developing effective machine learning models for suicidal ideation detection: access to large-scale, annotated datasets. To overcome this limitation, researchers propose an innovative strategy that leverages generative AI models like ChatGPT, Flan-T5, and Llama to create synthetic data for training. The approach is grounded in social factors extracted from psychology literature, aiming to ensure coverage of essential information related to suicidal ideation. The study benchmarks against state-of-the-art NLP classification models centered around the BERT family structures. The results show that synthetic data-driven methods offer consistent F1-scores comparable to those achieved by conventional models trained on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help machines understand and detect suicidal thoughts. Right now, it’s hard to get access to big datasets with information about suicide because of the sensitive nature of this topic. The researchers came up with an idea to use special AI models that can create fake data for training machine learning models. They used social factors from psychology to make sure the fake data covers important points about suicidal thoughts. They compared their method to others using real-world datasets and found that it works just as well! This is a big deal because it could help us develop better tools to support people’s mental health. |
Keywords
* Artificial intelligence * Bert * Classification * Llama * Machine learning * Nlp * Synthetic data * T5
