# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 2 Related Works  
Rapid advancements in LLMs in the field of artificial intelligence (AI) have sparked substantial academic and industry interest. A growing body of literature addresses the opportunities and challenges inherent in LLMs, focusing on regulatory frameworks, technical hurdles, domain specialization, and trustworthiness [15]. LLMs have transformed natural language processing (NLP) by achieving state-of-the-art (SOTA) performance on diverse tasks. The work of [21] provides an overview of this transition from task-specific architectures to general-purpose models capable of adapting across domains, emphasizing the role of the pre-training objectives, benchmarks, and transfer learning mechanisms of these models. Expanding on this, Ref. [4] analyzes the historical evolution, training techniques, and applications of LLMs in domains such as healthcare, education, and agriculture; Ref. [22] delves deeper into the mechanisms that enable LLMs to perform complex tasks. They highlight ongoing challenges related to their reasoning abilities, computational demands, and scalability, which remain critical. The work on domain-specialized LLMs presented in [23] introduces a taxonomy of domain-specialization techniques, application areas, and associated challenges, advocating customized approaches to harness the potential of LLMs in specialized fields. These findings align with [24], which analyzes research trends from 2017 to early 2023 in LLMs, analyzing 5000 publications to provide researchers, practitioners, and policy makers with a broad overview of the evolving landscape of LLMs, particularly in the fields of medicine, engineering, social science, and the humanities. These studies provide valuable insights into the advancements in LLMs, yet they fall short of systematically exploring more inclusive applications of LLMs across high-impact domains, such as the communication technologies, automotive, and robotics domains. Trustworthiness in LLMs is a key focus in the recent literature, with multiple studies addressing associated risks and mitigation strategies. In the work of [25], the authors review debiasing techniques and approaches to mitigate hallucinations, highlighting the importance of ethical oversight and evaluation through a comprehensive taxonomy of mitigation methods, complementing the work of [26] which emphasizes the importance of robust mechanisms to improve transparency and controllability in mitigating bias. Extending the discussion on trust assessment, Ref. [18] advocates for interdisciplinary collaboration to address societal bias, value alignment, and environmental sustainability concerns. In [27], the authors investigate the relationship between model compression and trustworthiness, finding that moderate quantization (e.g., 4-bit) maintains trustworthiness while improving efficiency, whereas excessive compression (e.g., 3-bit) degrades it. Unlike [25], which touches upon several aspects of data handling and pre-processing that relate to improving data quality, this study links algorithmic efficiency to ethical outcomes, showing how quantization choices affect fairness and ethics metrics. Furthermore, the work of [15] discusses varying approaches to AI regulation across different global regions, while our key focus lies on the EU AI Act. In addition to that, the discussion covers LLMs under General Purpose AI Models (GPAI) and details certain obligations imposed on them. Compared to our work, these studies collectively highlight crucial ethical risks and societal implications but lack a detailed examination of LLM trustworthiness based on the specific trustworthiness dimensions outlined in the recent regulatory framework, the EU AI Act. Regulatory frameworks for AI have been rapidly evolving with technological advancements and ever-growing public concerns. The EU AI Act is one of the first pioneering, legally binding, comprehensive, and horizontally applied regulatory frameworks for AI systems.