Summary of Using Gpt Models For Qualitative and Quantitative News Analytics in the 2024 Us Presidental Election Process, by Bohdan M. Pavlyshenko
Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process
by Bohdan M. Pavlyshenko
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 research paper presents an innovative approach to analyzing news about the 2024 US presidential election process using Google Search API and the GPT-4o model. The authors employed retrieval-augmented generation (RAG) to leverage both qualitative and quantitative analysis techniques. By applying this method to different news sources across various time periods, the researchers generated quantitative scores that were analyzed using Bayesian regression to derive trend lines and understand uncertainty in the election process. The study demonstrates the effectiveness of using GPT models for news analysis, providing valuable insights that can inform future analyses of election processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special computer model called Google Search API and GPT-4o to study news about the 2024 US presidential election. They want to understand what’s happening in the news and why it matters. The researchers looked at different sources of news from different time periods and used a special way of analyzing the data to see patterns and trends. This helps them understand how uncertain things are during an election process. Overall, this study shows that using these computer models can help us learn more about what’s happening in the news and make smart decisions. |
Keywords
» Artificial intelligence » Gpt » Rag » Regression » Retrieval augmented generation
