Updated citation to the IEEE BigData Document

README.md

@@ -152,13 +152,12 @@ This dataset is uniformly biased towards mathematics, computer science, cybersec
 **BibTeX:**
 
 ```bibtex
-@misc{tihanyi2024diabenchmarking,
-    title={Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence}, 
-    author={Norbert Tihanyi and Tamas Bisztray and Richard A. Dubniczky and Rebeka Toth and Bertalan Borsos and Bilel Cherif and Mohamed Amine Ferrag and Lajos Muzsai and Ridhi Jain and Ryan Marinelli and Lucas C. Cordeiro and Merouane Debbah},
-    year={2024},
-    eprint={2410.15490},
-    archivePrefix={arXiv},
-    primaryClass={cs.AI},
-    url={https://arxiv.org/abs/2410.15490}, 
-}
+@INPROCEEDINGS{diabench,
+  author={Tihanyi, Norbert and Bisztray, Tamas and Dubniczky, Richard A. and Toth, Rebeka and Borsos, Bertalan and Cherif, Bilel and Jain, Ridhi and Muzsai, Lajos and Ferrag, Mohamed Amine and Marinelli, Ryan and Cordeiro, Lucas C. and Debbah, Merouane and Mavroeidis, Vasileios and Jøsang, Audun},
+  booktitle={2024 IEEE International Conference on Big Data (BigData)}, 
+  title={Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence}, 
+  year={2024},
+  pages={3313-3321},
+  keywords={Measurement;Adaptation models;Computational modeling;Benchmark testing;Reliability engineering;Mathematical models;Data models;Reliability;Problem-solving;Computer security;Artificial Intelligence;Large Language Models;Dynamic Benchmarking;Performance Metrics;Reliability},
+  doi={10.1109/BigData62323.2024.10825051}}
 ```