Summary of Global-liar: Factuality Of Llms Over Time and Geographic Regions, by Shujaat Mirza et al.
Global-Liar: Factuality of LLMs over Time and Geographic Regions
by Shujaat Mirza, Bruno Coelho, Yuyuan Cui, Christina Pöpper, Damon McCoy
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 investigates the factuality and fairness of Large Language Models (LLMs) like GPT-3.5 and GPT-4 in retrieving information online. It evaluates their factual accuracy, stability, and biases, aiming to improve the reliability and integrity of AI-mediated information dissemination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well language models like GPT-3.5 and GPT-4 get facts right, stay consistent, and avoid being unfair or biased. This is important because we rely on these models for online information a lot, and we want to make sure they’re giving us trustworthy answers. |
Keywords
» Artificial intelligence » Gpt
