Summary of Compl-ai Framework: a Technical Interpretation and Llm Benchmarking Suite For the Eu Artificial Intelligence Act, by Philipp Guldimann et al.
COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act
by Philipp Guldimann, Alexander Spiridonov, Robin Staab, Nikola Jovanović, Mark Vero, Velko Vechev, Anna-Maria Gueorguieva, Mislav Balunović, Nikola Konstantinov, Pavol Bielik, Petar Tsankov, Martin Vechev
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); 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 The paper presents COMPL-AI, a framework that translates the European Union’s Artificial Intelligence Act into measurable technical requirements, focusing on large language models (LLMs). The framework includes an open-source benchmarking suite based on state-of-the-art LLM benchmarks. By evaluating 12 prominent LLMs using COMPL-AI, the authors reveal shortcomings in existing models and benchmarks, particularly in robustness, safety, diversity, and fairness. This highlights the need for a shift towards these aspects, encouraging balanced development of LLMs and regulation-aligned benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explains how the European Union’s Artificial Intelligence Act needs to be made more specific so that it can be used as a guide for developing artificial intelligence (AI) models. The authors created a system called COMPL-AI that takes the general rules from the AI Act and turns them into technical requirements that can be measured. They also built an open-source set of tests, or benchmarks, based on the best existing ways to test LLMs. By testing 12 popular LLMs using these new standards, the authors showed what areas need improvement, such as making sure AI models are robust and fair. |
