A holistic investigation into the foundational lineage of Large Language Models (LLMs). Covers Transformer evolutions, mixture-of-experts (MoE), RLHF/DPO alignment, synthetic data curation, agentic orchestration frameworks, parameter-efficient adaptations, and deep ethical-safety paradigms.
@article{sajjadi2025llm,
title = {A Survey of Large Language Models: Evolution, Architectures, Adaptation, Benchmarking, Applications, Challenges, and Societal Implications},
author = {Sajjadi Mohammadabadi, Seyed Mahmoud and Kara, Burak Cem and Eyupoglu, Can and Uzay, Can and Tosun, Mehmet Serkan and Karaku{\c{s}}, Oktay},
journal = {Electronics},
volume = {14},
number = {18},
pages = {3580},
year = {2025},
publisher = {MDPI},
doi = {10.3390/electronics14183580}
}