Summary of Llm Detectors Still Fall Short Of Real World: Case Of Llm-generated Short News-like Posts, by Henrique Da Silva Gameiro et al.
LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts
by Henrique Da Silva Gameiro, Andrei Kucharavy, Ljiljana Dolamic
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); 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 investigates the effectiveness of Large Language Model (LLM) detectors in identifying disinformation generated by LLMs. Specifically, it focuses on a critical scenario where moderately sophisticated attackers create short news-like posts to spread misinformation. The study demonstrates that current LLM detectors are not sufficient to combat this issue, emphasizing the need for more robust solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can be used to spread false information. Right now, there are tools designed to detect these kinds of attacks, but they don’t work very well in real-life situations. The researchers looked at a specific type of attack where someone creates short news articles that seem true, but aren’t. They found out that the current tools aren’t good enough and we need better ways to stop this kind of misinformation. |
Keywords
* Artificial intelligence * Large language model
