Summary of Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models, by Abdurahmman Alzahrani et al.
Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models
by Abdurahmman Alzahrani, Eyad Babkier, Faisal Yanbaawi, Firas Yanbaawi, Hassan Alhuzali
First submitted to arxiv on: 21 May 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 presents a comprehensive empirical study focused on identifying persuasive techniques in Arabic social media content using Pre-trained Language Models (PLMs) and the ArAlEval dataset. The authors leverage three different learning approaches: feature extraction, fine-tuning, and prompt engineering techniques to analyze the presence or absence of persuasion techniques, as well as their specific types. The study finds that the fine-tuning approach yields the highest results on the dataset, achieving an f1-micro score of 0.865 and an f1-weighted score of 0.861. Additionally, the authors demonstrate the potential for enhancing the performance of the GPT model through few-shot learning techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to identify persuasive techniques in Arabic social media posts using special language models called Pre-trained Language Models (PLMs). The researchers used a big dataset called ArAlEval that had two tasks: one was to see if persuasion techniques were present or not, and the other was to figure out what kind of techniques were being used. They tried three different ways to do this: feature extraction, fine-tuning, and prompt engineering. They found that fine-tuning worked best, with an accuracy of 86.5%. They also showed that by using a few examples, they could make the GPT model work better. |
Keywords
» Artificial intelligence » Feature extraction » Few shot » Fine tuning » Gpt » Prompt
