# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### Feasibility and Scalability of Privacy-Preserving Techniques  
Implementing privacy safeguards for LLMs exceeding 100 billion parameters poses daunting computational hurdles. Differential privacy (DP) methods, such as DP-SGD, require computing and clipping per-sample gradients, which becomes prohibitively expensive at scale [43; 44]. Even when feasible, DP often incurs notable utility losses, with some large-model experiments showing a 10-20% accuracy gap versus non-private baselines [44]. Machine unlearning faces similar challenges: fully retraining a gigantic model to \"forget\" a single dataset is prohibitively time-consuming, and approximate fine-tuning approaches risk degrading unrelated functionality [45]. Many real-world deployments thus rely on partial solutions, like filtering user-specific outputs rather than fully erasing internal representations [46].  
Recent work suggests potential mitigations. For instance, a Mixture-of-Experts approach allows selectively \"removing\" or retraining only an expert sub-module, decreasing the scope of full-model updates [47]. Likewise, selective DP applies noise only to truly sensitive training steps, reducing overhead and preserving more utility [48]. While these techniques show promise on smaller to medium-scale benchmarks, the path to seamless, large-scale unlearning and high-utility differential privacy remains an open research area. Efforts continue to balance stringent privacy requirements with practical computational costs and minimal accuracy loss.  
**Practical Examples of Machine Unlearning and Bias Mitigation.** Recent studies have investigated techniques for selectively removing or \"unlearning\" personal data from trained machine learning models to comply with regulations such as the GDPR's Right to Erasure. [PERSON] et al. (2020) presented methods for selective forgetting in deep neural networks, demonstrating effective data removal with minimal degradation in model performance on image classification tasks [49]. [PERSON] et al. (2021) proposed a systematic approach for machine unlearning, employing modular architectures to efficiently delete specific training instances without significant retraining overhead [31]. In terms of bias mitigation, empirical analyses have highlighted effective techniques for debiasing large pretrained language models. For example, [PERSON] et al. (2020) provided evidence that targeted fine-tuning significantly reduces gender-based stereotypes in language models like BERT [50]. Additionally, [PERSON] and [PERSON] (2021) introduced efficient debiasing methods for contextualized embeddings, achieving measurable decreases in biases while preserving the overall effectiveness of the embeddings [51]. These studies collectively underscore the potential and current challenges in developing practical technical solutions to enhance privacy and fairness in machine learning applications.  
These concerns necessitate a careful examination of the intersection between LLMs and data protection laws to ensure that the deployment of such technologies aligns with legal and ethical standards.