Episode Details

Gold data means human-verified. Silver means machine-annotated. But what actually separates them, and how much human checking do you need before a generated batch becomes trustworthy? This episode digs into the staged workflow of generating data with an LLM, correcting a small sample by hand, then scaling up — and why a single correction can reveal you've been generating the wrong kind of example entirely. Along the way: why "gold standard" is borrowed from medicine, how the CALBC project reconciled multiple named-entity taggers into a consensus back around 2010, why agreement between models can be cheap when they share the same blind spots, and what a three-seed experiment with an 8B Llama model on coding benchmarks actually proves. If you've ever wondered whether your reviewed examples count as ground truth — or whether you're doing active learning or just sampling — this one's for you. Key sources: • Hahn, Tomanek, Beisswanger & Faessler, A Proposal for a Configurable Silver Standard, ACL Linguistic Annotation Workshop 2010 — https://aclanthology.org/W10-1838.pdf • Wang et al., Self-Instruct: Aligning Language Models with Self-Generated Instructions, arXiv:2212.10560v2 (ACL 2023) — https://arxiv.org/abs/2212.10560v2 • Zhu, Asawa, Davis et al., BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation, arXiv:2502.01697v3 (2025) — https://arxiv.org/abs/2502.01697v3 Episode 6240 — myweirdprompts.com/n/6240 (permanent short link: myweirdprompts.com/123873)