mlsanityData to 7 Oct 2026

AI Act for the Working Programmer

arXiv 2408.01449 ↗First posted 23 Jul 2024cs.CY cs.AI cs.SEcited by 1

Not on the map. Why?

Abstract (arXiv)

The European AI Act is a new, legally binding instrument that will enforce certain requirements on the development and use of AI technology potentially affecting people in Europe. It can be expected that the stipulations of the Act, in turn, are going to affect the work of many software engineers, software testers, data engineers, and other professionals across the IT sector in Europe and beyond. The 113 articles, 180 recitals, and 13 annexes that make up the Act cover 144 pages. This paper aims at providing an aid for navigating the Act from the perspective of some professional in the software domain, termed "the working programmer", who feels the need to know about the stipulations of the Act.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

27 of 35 references.

References · 27

  1. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  4. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,106
  5. 2020AdapterFusion: Non-Destructive Task Composition for Transfer Learningcited by 780
  6. 2020Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuningcited by 658
  7. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 641
  8. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMscited by 533
  9. 2024The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearningcited by 488
  10. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  11. 2020Parameter-Efficient Transfer Learning with Diff Pruningcited by 351
  12. 2023LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 317
15 more
  1. 2024TrustLLM: Trustworthiness in Large Language Modelscited by 280
  2. 2022DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptationcited by 229
  3. 2019REMIND Your Neural Network to Prevent Catastrophic Forgettingcited by 197
  4. 2022Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teachercited by 189
  5. 2020Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgettingcited by 169
  6. 2021UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuningcited by 162
  7. 2023Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERTcited by 155
  8. 2023Bayesian Low-rank Adaptation for Large Language Modelscited by 112
  9. 2023Combating Misinformation in the Age of LLMs: Opportunities and Challengescited by 94
  10. 2020GAN Memory with No Forgettingcited by 84
  11. 2021Demystifying the Draft EU Artificial Intelligence Actcited by 82
  12. 2023Parameter-Efficient Fine-Tuning Design Spacescited by 67
  13. 2019Growing a Brain: Fine-Tuning by Increasing Model Capacitycited by 43
  14. 2019Learning to Forget for Meta-Learningcited by 35
  15. 2022Efficient Fine-Tuning of BERT Models on the Edgecited by 29

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbationscited by 0