Summary of Knowledge-augmented Reasoning For Euaia Compliance and Adversarial Robustness Of Llms, by Tomas Bueno Momcilovic et al.
Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs
by Tomas Bueno Momcilovic, Dian Balta, Beat Buesser, Giulio Zizzo, Mark Purcell
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Software Engineering (cs.SE)
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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 introduces a functional architecture that bridges the gap between adversarial robustness and compliance with the EU AI Act (EUAIA) for large language models (LLMs). It proposes a three-layer system consisting of detection, reporting, and reasoning layers. The detection layer is based on existing literature, while the reporting layer meets regulatory requirements. The reasoning layer utilizes knowledge augmentation to provide assurance cases and contextual mappings. This architecture aims to support developers and auditors in ensuring LLMs deployed in the EU are both compliant and adversarially robust, thereby promoting trustworthiness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create trustworthy AI systems that follow European rules and can defend against bad attacks. It suggests a special structure for big language models (LLMs) to make sure they meet these requirements. The system has three parts: one checks for problems, another reports findings, and the last part uses extra knowledge to explain why things are okay or not. |
