# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 5 Results and Discussion
### Results of RQ3  
**Research Question:** What are the emerging trends and developments in addressing trustworthiness in LLMs? **Objective:** Track trends over time, identifying shifts in focus or methodology regarding LLM trustworthiness. Analysis and Results  
Figure 12 shows the number of papers published on LLM trustworthiness from 2019 to June 2024. * In **2019**, there were 46 papers, indicating early research interest in LLM trustworthiness. * This number grew to **94 papers** in **2020**, showing increased attention to the topic. * In **2021**, the publications sharply rose to **212 papers**, and by **2022**, this number further increased to **327 papers**. * The trend peaked in **2023** with **1095 papers**, reflecting a significant surge in research on LLM trustworthiness. * In **2024**, the number slightly decreased to **960 papers**, but still remains high. This decrease can be attributed to the fact that we considered data only up to June 2024, which explains the slightly lower number compared to the peak of 2023. Despite this, the data shows a promising, massive increase in the number of papers, indicating strong and ongoing interest in the area. These trends indicate that research in LLM trustworthiness has grown significantly over the years, with a marked increase in interest around 2021 and a peak in 2023. Figure 11: Year-wise trend for application domains in 2024 (up to June). Figure 13 shows the distribution of trustworthiness aspects in LLM research. * In **2019**, the focus was on **accuracy** with 30 papers, followed by **data and data governance** (20 papers) and **transparency** (12 papers). * In **2020**, **accuracy** rose to 67 papers, and **transparency** increased to 21 papers, reflecting growing interest in these areas. * In **2021**, **accuracy** saw a significant jump to 126 papers, with **robustness** reaching 39 papers, highlighting the growing emphasis on these aspects. * The trend continued in **2022**, with **accuracy** at 219 papers and **transparency** reaching 124 papers, alongside a notable increase in **human oversight** (52 papers). * In **2023**, **accuracy** surged to 631 papers, **robustness** to 288 papers, and **transparency** to 405 papers, marking the highest level of research attention. * In **2024**, we only considered papers up to June, where **accuracy** remained at 420 papers, and **robustness** decreased to 137 papers. However, there was still a strong focus on **human oversight** (334 papers) and **data governance** (202 papers). These trends show a significant increase in research on **accuracy**, **transparency**, and **robustness**, with growing attention to **human oversight** in recent years. Figure 14 illustrates yearly trends for the top 3 most relevant LLMs in terms of occurrences related to trustworthiness research: GPT, BERT, and LLM. * **GPT:** Research on GPT's trustworthiness has grown significantly, starting with **7 papers in 2019** and gradually increasing to **530 papers in 2023**, before a slight decline to **467 papers in 2024**. * **BERT:** Interest in BERT began earlier, with **39 papers in 2019**, and reached its peak in **2023** with **275 papers**, followed by a decline to **146 papers in 2024**. * **LLM:** Research on the general LLM category had a slow start with **4 papers in 2019**, but experienced rapid growth from **71 papers in 2022** to **298 papers in 2023**, and further rising to **387 papers in 2024**. Figure 15 shows trends for additional LLMs such as T5, BARD, LLaMa, and others. * **T5:** Research on T5 started in **2020** with **3 papers** and peaked at **30 papers in 2023**, but saw a decline to **12 papers in 2024**. * **BARD:** Research on BARD began only in **2023** with **20 papers**, increasing to **34 papers in 2024**, reflecting growing interest. * **LLaMa:** LLaMa-related trustworthiness research commenced in **2023** with **23 papers**, rising to **31 papers in 2024**. * **Other LLMs:*
* From **4 papers in 2020*
* to **22 papers in 2023**, followed by a sharp drop to **4 papers in 2024**.