Summary of Are You Human? An Adversarial Benchmark to Expose Llms, by Gilad Gressel et al.
Are You Human? An Adversarial Benchmark to Expose LLMs
by Gilad Gressel, Rahul Pankajakshan, Yisroel Mirsky
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a method to detect Large Language Models (LLMs) in real-time by designing text-based prompts that challenge their ability to follow instructions and perform tasks. The authors compile an open-source benchmark dataset containing “implicit challenges” that exploit LLMs’ instruction-following mechanism to cause role deviation, and “explicit challenges” that test their ability to complete simple tasks. Evaluation of 9 leading models from the LMSYS leaderboard shows that explicit challenges successfully detected LLMs in 78.4% of cases, while implicit challenges were effective in 22.9% of instances. User studies validate the real-world applicability of the methods, with humans outperforming LLMs on explicit challenges (78% vs 22% success rate). The framework also reveals that many participants use LLMs to complete tasks, demonstrating its effectiveness in detecting both AI impostors and human misuse of AI tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to tell if you’re talking to a computer or a person. Large Language Models (LLMs) are very good at pretending to be people, but that’s not always okay. The authors created special tests to figure out when someone might be an LLM. They tested 9 different models and found that some tests were better than others at catching the LLMs. People did better on these tests than the computers! This is important because we need ways to make sure we’re talking to real people, not just pretending ones. |
