# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 1 Introduction  
The development of large language models (LLMs) has been driven by the advancements in deep learning [1, 2], the availability of vast public datasets [3, 4, 5], and powerful resources capable of processing big data with complex algorithms. These models have significantly enhanced the machines' ability to process and understand the context in long sequences of text, allowing them to grasp the nuances and generate human-like responses [6]. In particular, Google's Bidirectional Encoder Representations from Transformers (BERT) [7] and Open AI's Generative Pre-trained Transformer (GPT) series have revolutionized natural language processing by excelling at various language tasks in various applications [8]. Moreover, fine-tuning LLMs on specific downstream tasks led to their widespread adoption in industries like customer service [9], healthcare, education [10], and finance [11]. Despite their remarkable capabilities, the increasing deployment of these models in high-stake domains raised significant concerns regarding their trustworthiness, primarily due to their propensity for hallucinations and inherent biases. As a result, there is a growing emphasis on establishing principles for the responsible development and deployment of LLMs. The EU Trustworthy AI framework [12] outlines core principles such as fairness, transparency, accountability, and safety. Complementing this, the EU AI Act [13] which came into force on August 2024 is considered the first-ever legal framework on AI that defines seven key attributes of trustworthiness such as human oversight, record-keeping, data and data governance, transparency, accuracy, robustness, and cybersecurity. This Act categorizes AI systems by risk levels, imposing strict requirements on high-risk applications to protect human rights and safety. Moreover, to facilitate the implementation of the law, the European Commission has authorized the CEN and CENELEC Joint Technical Committee 21 (JTC 21) to develop harmonized standards that will provide companies with a legal presumption of conformity [14]. Unlike existing strategies around AI governance by principal players (United States, China, Canada, United Kingdom, and The G7), the EU AI Act introduces clear, enforceable obligations for high-risk AI systems and a sophisticated product safety framework in a hierarchical structure [15]. Through harmonized standards, the AI Act codifies the EU's rigorous requirements for trustworthy AI, providing a unified framework that serves as a global benchmark in the field. Furthermore, Ref. [15] discusses the EU AI Act, emphasizing the importance of interdisciplinary approaches, public trust, and ethical considerations in the development and regulation of AI systems to ensure their responsible deployment and societal benefits. However, many LLMs do not meet these standards due to limitations in tailored methodologies or data availability [16]. For example, while proprietary models often outperform open source ones in certain dimensions, such as safety, they still fall short in areas such as bias mitigation and transparency [16]. Moreover, innovative approaches like the Think-Solve-Verify (TSV) framework have demonstrated improvements in reasoning tasks but highlight persistent challenges in introspective self-awareness and collaborative reasoning [17]. Investigating limitations and shortcomings of LLMs is crucial for understanding where and what trustworthiness measures are most needed [18, 19]. For instance, untrustworthy models in telecommunications could lead to operational failures or privacy breaches, erroneous problem-solving, resource misallocation, etc. Similarly, in healthcare or education, biased or unreliable outputs could undermine public confidence or exacerbate inequalities. The primary objective of this study is to perform a detailed analysis to assess the extent to which large language models adhere to the trustworthiness principles set forth in the EU AI Act [13].