Summary of Using Generative Agents to Create Tip Sheets For Investigative Data Reporting, by Joris Veerbeek and Nicholas Diakopoulos
Using Generative Agents to Create Tip Sheets for Investigative Data Reporting
by Joris Veerbeek, Nicholas Diakopoulos
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 The paper presents an innovative approach using generative AI agents to create tip sheets for investigative data reporting. The system involves three specialized agents: an analyst, a reporter, and an editor, which collaborate to generate and refine tips from datasets. Compared to a baseline model without agents, the agent-based system generates more newsworthy and accurate insights, with some variability noted between different stories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to help journalists find important information for their stories. It creates a special team of AI “agents” that work together to generate ideas for investigative reporting. The results show that this approach is better than just using one model alone, and can even find new leads that human reporters might miss. |
