Summary of Large Language Models For Automatic Detection Of Sensitive Topics, by Ruoyu Wen et al.
Large Language Models for Automatic Detection of Sensitive Topics
by Ruoyu Wen, Stephanie Elena Crowe, Kunal Gupta, Xinyue Li, Mark Billinghurst, Simon Hoermann, Dwain Allan, Alaeddin Nassani, Thammathip Piumsomboon
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper explores the capabilities of five large language models (LLMs) for detecting sensitive messages in online content related to mental well-being. The study uses two datasets and evaluates the performance of the LLMs in terms of accuracy, precision, recall, F1 scores, and consistency. The results show that LLMs have the potential to be used as a precise detection tool in content moderation workflows. The best-performing model, GPT-4o, achieved an average accuracy of 99.5% and an F1-score of 0.99. The study discusses the advantages and potential challenges of using LLMs in moderation workflows and suggests that future research should address the ethical considerations of utilising this technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping online communities stay safe by using big language models to detect sensitive messages. This could help human moderators focus on important tasks instead of doing tedious work. The study tested five language models to see how well they can find sensitive messages in two datasets. The results show that these models are very good at detecting sensitive messages and could be used to make online communities safer. |
Keywords
» Artificial intelligence » F1 score » Gpt » Precision » Recall
