Summary of Can Adversarial Attacks by Large Language Models Be Attributed?, By Manuel Cebrian and Jan Arne Telle
Can adversarial attacks by large language models be attributed?
by Manuel Cebrian, Jan Arne Telle
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY); Formal Languages and Automata Theory (cs.FL)
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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 abstract investigates the problem of attributing outputs from Large Language Models (LLMs) in adversarial settings, such as cyberattacks and disinformation. It uses formal language theory to analyze whether finite text samples can uniquely pinpoint the originating model. The results show that it is theoretically impossible to attribute outputs to specific LLMs with certainty due to non-identifiability of certain language classes and expressivity limitations of Transformer architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how to identify which Large Language Model (LLM) generated a piece of text when the models are trying to deceive or mislead. It uses special math ideas from language theory to understand if we can figure out which LLM made a certain piece of text just by looking at it. The research shows that this is actually impossible to do with certainty, even if we have all the details about the models and how they work. |
Keywords
» Artificial intelligence » Large language model » Transformer
