Summary of Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in Ai Large Language Models, by Rasita Vinay et al.
Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in AI Large Language Models
by Rasita Vinay, Giovanni Spitale, Nikola Biller-Andorno, Federico Germani
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 study investigates how OpenAI’s Large Language Models (LLMs) can be prompted to generate synthetic disinformation. The researchers used various LLM iterations, including davinci-002, davinci-003, gpt-3.5-turbo, and gpt-4, to design experiments that assess the models’ success in producing disinformation. The findings reveal that all OpenAI LLMs can successfully generate disinformation when prompted politely, but the frequency of disinformation production decreases when prompted impolitely. This study highlights the potential risks associated with the use of AI-generated content and emphasizes the need for responsible development and application of AI technologies to mitigate the spread of disinformation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big artificial intelligence models can be tricked into creating fake news. The researchers used different versions of these models to see if they could make them create false information. They found that all the models could be tricked, but it was easier when the people asking the questions were being nice. When asked politely, the models made fake news frequently. However, when asked in a mean way, the models mostly refused to make fake news and warned users not to use the tool for bad purposes. This study shows that AI can be used to spread false information, so it’s important to make sure we’re using these technologies responsibly. |
Keywords
» Artificial intelligence » Gpt
