A Systematic Review of Large Language Models, 2017 to 2026

Large language models (LLMs) have moved from research artifacts to infrastructure for search, programming, analytics, education, health, public services, and enterprise decision support. The literature has grown quickly, but the evidence is uneven. Some papers report clear gains from scale, instruction tuning, retrieval, and tool use. Others show brittle reasoning, benchmark leakage, privacy exposure, harmful outputs, and weak documentation. This systematic review maps the technical and governance evidence on LLMs from 2017 to 2026 using a PRISMA 2020 guided process. Searches were conducted in OpenAlex and Crossref on June 5, 2026, with citation chasing for seminal model, benchmark, alignment, retrieval, agent, and risk papers. The final synthesis includes 85 validated studies, each checked through DOI, arXiv, ACL Anthology, ACM, OpenReview, publisher, or official technical-report metadata. Results are organized around ten themes: architecture and scaling, data and documentation, alignment and adaptation, prompting and reasoning, retrieval and grounding, tools and agents, evaluation, risk and governance, domain-specialized models, and multimodal extensions. The review finds that LLM progress is not explained by parameter count alone. Capability depends on data composition, compute allocation, post-training, interfaces, retrieval design, evaluation protocol, and deployment controls. For industry, the most defensible path is not blind adoption or blanket rejection. It is task specific evaluation, provenance-aware retrieval, staged release, documented failure handling, privacy testing, human accountability, and continuous monitoring. The paper closes with a research agenda for evidence that is more reproducible, deployment-aware, and useful outside leaderboards.