this is getting tedious

references.bib

@@ -12,7 +12,25 @@ ISSN = {2076-3417},
 ABSTRACT = {Creating learning models that can exhibit sophisticated reasoning abilities is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, datasets, and benchmarks specifically designed to tackle mathematical problems, reporting impressive achievements in disparate fields such as automated theorem proving, numerical integration, and the discovery of new conjectures or matrix multiplication algorithms. However, despite this notable success it is still unclear whether deep learning models possess an elementary understanding of quantities and numbers. This survey critically examines the recent literature, concluding that even state-of-the-art architectures and large language models often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge.},
 DOI = {10.3390/app14020744}
 }
-
-
+@article{Wang2024, title={History, development, and principles of large language models: An introductory survey}, DOI={10.1007/s43681-024-00583-7}, journal={AI and Ethics}, author={Wang, Zichong and Chu, Zhibo and Doan, Thang Viet and Ni, Shiwen and Yang, Min and Zhang, Wenbin}, year={2024}, month={Oct}}
+@misc{vaswani2023attentionneed,
+      title={Attention Is All You Need}, 
+      author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
+      year={2023},
+      eprint={1706.03762},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/1706.03762}, 
+}
+
+@misc{ivanov2024,
+      title={AI Benchmarks and Datasets for LLM Evaluation}, 
+      author={Todor Ivanov and Valeri Penchev},
+      year={2024},
+      eprint={2412.01020},
+      archivePrefix={arXiv},
+      primaryClass={cs.DC},
+      url={https://arxiv.org/abs/2412.01020}, 
+}