Summary of Towards Assurance Of Llm Adversarial Robustness Using Ontology-driven Argumentation, by Tomas Bueno Momcilovic et al.
Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation
by Tomas Bueno Momcilovic, Beat Buesser, Giulio Zizzo, Mark Purcell, Dian Balta
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed novel approach ensures the adversarial robustness of large language models (LLMs) using formal argumentation. By structuring state-of-the-art attacks and defenses through ontologies, it facilitates the creation of a human-readable assurance case and machine-readable representation. The application is demonstrated in English language and code translation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Despite challenges in ensuring the security, transparency, and interpretability of large language models (LLMs), a novel approach proposes formal argumentation for assurance of adversarial robustness. This is achieved by structuring state-of-the-art attacks and defenses through ontologies, creating human-readable and machine-readable representations. The method demonstrates its application in English language and code translation tasks. |
Keywords
» Artificial intelligence » Translation
