# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 5 Results and Discussion
### Results of RQ3
* **XLNet:** Showed moderate growth, with **3 papers in 2020**, peaking at **13 papers in 2023**, and dropping to **4 papers in 2024**. * **CLAUDE:** Research began in **2023** with **7 papers**, rising to **19 papers in 2024**. * **BingAI and PaLM:** Both had consistent yet modest interest, with **nine and eight papers, respectively, in 2023 and 2024**. * **Emerging Models:** Models like **GEMINI** and **Llama** saw limited but increasing interest in 2024, e.g., GEMINI with **13 papers** and Llama with **10 papers**. The trends highlight a sustained focus on GPT and BERT, with the general LLM category gaining momentum in recent years. Emerging models such as BARD, LLaMa, and CLAUDE have also gained traction, signaling a diversification in research priorities. Models like T5 and XLNet show fluctuating interest, while others, including GEMINI and MISTRAL, are witnessing initial growth. These shifts emphasize the evolving landscape of trustworthiness research in LLMs. Figure 14: Trends for GPT, BERT, and LLM research on trustworthiness from 2019 to June 2024. Trends in LLM trustworthiness research from 2022 to June 2024 reveal interesting dynamics in the evaluation of various language models. In 2022, as shown in Figure 16, among the top LLMs, **BERT** dominated with 197 occurrences, accounting for **54.7%** of the total focus, followed by **LLM** with 71 occurrences (**19.7%**). **GPT** also received significant attention with 46 occurrences (**12.8%**). Other notable LLMs include **BART** (14 occurrences, **3.9%**) and **T5** (13 occurrences, **3.6%**). Smaller contributions were made by **XLNet** (10 occurrences, **2.8%**), **ERNIE** (4 occurrences, **1.1%**), **LAMDA** (2 occurrences, **0.6%**), and other models like **BLOOM**, **ELECTRA**, and **Copilot**, each accounting for less than **0.5%**. The trends shifted significantly in 2023, as depicted in Figure 17. The most notable change was the substantial increase in the focus on **GPT**, which surged to 530 occurrences, constituting **41.4%** of the total interest. **LLM** also experienced notable growth, reaching 298 occurrences (**23.3%**). **BERT** maintained its relevance with 275 occurrences (**21.5%**). Other LLMs included **T5** (30 occurrences, **2.3%**), **LLMa** (23 occurrences, **1.8%**), **BARD** (20 occurrences, **1.6%**), and **BART** (22 occurrences, **1.7%**). Emerging models like **CLAUDE**, **PaLM**, and **BingAI** had modest but significant occurrences (ranging from **0.5%** to **0.7%**). Models such as **MISTRAL**, **CodeGen**, and **Llama** accounted for less than **0.3%** each. In 2024, as shown in Figure 18, **GPT** continued to lead with 467 occurrences, making up **39.7%** of the total focus. **LLM** saw a slight increase to 387 occurrences (**32.9%**). However, **BERT** experienced a notable decline, with 146 occurrences (**12.4%**). Other LLMs such as **LLMa** (31 occurrences, **2.6%**), **BARD** (34 occurrences, **2.9%**), and **CLAUDE** (19 occurrences, **1.6%**) played smaller but growing roles. Emerging models such as **GEMINI** (13 occurrences, **1.1%**), **Llama** (10 occurrences, **0.9%**), and **PaLM** (8 occurrences, **0.7%**) reflect a diversifying research focus. Models like **DALL-E**, **MISTRAL**, and **FALCON** each accounted for **0.1% to 0.4%**, indicating initial but limited attention. The trends across the three years show a clear dominance of **GPT**, followed by **LLM** and **BERT**, although their relative contributions vary over time. **GPT** experienced exponential growth, especially from 2022 to 2023, while **LLM** also gained momentum. **BERT**, despite its early dominance, saw a steady decline. Emerging models such as **LLMaMa**, **BARD**, **CLAUDE**, and **GEMINI** reflect the growing diversity of LLMs. Smaller models like **T5**,  
Figure 15: Trends for other LLMs (T5, BARD, LLMa, etc.) research on trustworthiness from 2019 to June 2024. BART, and **XLNet** retained some relevance, while newer entrants such as **MISTRAL** and **ChatGLM** highlight evolving trends in the LLM trustworthiness ecosystem.