# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 7 Conclusions and Future Directions  
Our systematic mapping study reveals an exponential increase in research on the trustworthiness of large language models, particularly since 2021, peaking in 2023 and maintaining strong interest throughout 2024. GPT and BERT dominate the landscape, while newer models like Mistral and Claude remain underexplored. However, emerging models such as LLaMa, BARD, and Claude are gradually gaining traction, signaling a shift in research focus. Furthermore, in alignment with the trustworthiness aspects outlined in the landmark legislation, the EU AI Act, the current research on LLMs primarily focuses on accuracy and transparency, while critical aspects such as cybersecurity and record-keeping remain underexamined. Although linguistics, healthcare, and education are the most studied domains due to their social impact, critical areas such as telecommunications, cybersecurity, finance, and the environment remain significantly underrepresented. In addition, emerging technological fields such as IoT, automotive systems, and smart factories present substantial research gaps, offering promising directions for future exploration. The observed focus on certain LLMs, their application domains, and trustworthiness dimensions can be driven by several factors. Models usually vary in terms of their accessibility (documentation; communities), impact (adoption in many applications), and benchmarking (availability of standardized tools), which may gravitate the research direction towards more visible models and evaluating them from relatively urgent aspects. Nevertheless, with the AI Act set to take full effect in 2027, it can be expected to shape the trajectory and pace of technological development, particularly by intensifying the focus on the deployment of more transparent and accountable systems across sectors. The regulation mandates the integration of security by design and the maintenance of comprehensive technical documentation to facilitate regulatory oversight and foster public trust. As a result, these provisions may prove especially influential in shedding light on critical but underexplored areas. Most studies prioritize solution proposals and effectiveness evaluations, with limited emphasis on conceptual or experience-based approaches. Academia drives most of this research, with minimal industry and government involvement, highlighting the need for greater collaboration. To ensure the trustworthiness of LLMs in diverse applications, a more balanced research approach is needed. Future research can focus on systematically exploring underrepresented high-risk domains to uncover how trustworthiness requirements vary and interact with the specific technical, ethical, and regulatory challenges of LLM deployment in different contexts. However, there can be different ways of conducting the analysis via examining established or new model architectures to identify whether certain architectural choices improve interpretability or reasons behind certain behavior patterns. Additionally, while this study is centered on the AI Act, which represents the most comprehensive and prominent framework, a comparative analysis of emerging AI regulations in other regions can provide a well-rounded understanding of global governance. In the context of rapidly evolving technologies like LLMs, practitioners are encouraged to develop sector-specific approaches to supplement horizontal AI trustworthiness requirements. These practices should account for contextual risks and application-specific constraints to explore how best to operationalize trustworthiness across different domains. Furthermore, given the multidimensional nature of trustworthiness, it is essential for policymakers to foster interdisciplinary collaboration, as fragmentation across disciplines limits the integration of diverse perspectives, often resulting in incomplete solutions that fall short of addressing the complexity of real-world applications.