Summary of Evaluating the Efficacy Of Large Language Models in Detecting Fake News: a Comparative Analysis, by Sahas Koka et al.
Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis
by Sahas Koka, Anthony Vuong, Anish Kataria
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper evaluates the effectiveness of various Large Language Models (LLMs) in identifying and filtering fake news content. It compares four large LLMs, including GPT-4, Claude 3 Sonnet, Gemini Pro 1.0, and Mistral Large, as well as two smaller LLMs, Gemma 7B and Mistral 7B. The study uses a comparative analysis approach and fake news dataset samples from Kaggle to shed light on the current capabilities and limitations of LLMs in fake news detection. This research has implications for developers and policymakers seeking to enhance AI-driven informational integrity. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well different artificial intelligence models can spot fake news. It compares many big models, like GPT-4 and Claude 3 Sonnet, with some smaller ones, Gemma 7B and Mistral 7B. They test these models using samples of fake news from a website called Kaggle. This research helps us understand how good these AI models are at finding fake news and what we can do to make them better. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt