Order these instruction datasets from highest to lowest quality per example

This question is about **quality per example**, not total size. One LIMA example teaches the model more than one Alpaca example, even though Alpaca has 50 times as many. The ranking tracks how directly humans shaped each example. At the top sits LIMA: a thousand examples hand-curated by researchers, every one polished. Its whole thesis is that a small clean set beats a large noisy one. Next comes OpenAssistant, real volunteers writing and rating multi-turn conversations. Then Dolly v2, written by Databricks employees on the job, human but produced faster and with less curation. Lower down is FLAN v2. It is clean, but it is built by pouring old benchmark datasets into templates, so examples are repetitive and narrow in what each one teaches. Last is Alpaca: an older model generated its own instructions with only light filtering, so each example carries the most noise. The lesson: a model learns the average quality of what it sees, so curation per example beats raw volume.

The correct order is LIMA, then OpenAssistant, then Dolly v2, then FLAN v2, then Alpaca. The single axis is **quality per example**, which tracks how directly and carefully humans authored each item. LIMA leads by design. It is a thousand examples, hand-curated to maximize per-example signal. OpenAssistant follows: volunteers wrote and ranked real multi-turn dialogues, so quality is high but more variable. Dolly v2 sits next, written by employees, genuinely human but produced quickly with light curation. FLAN v2 ranks below the human-written sets despite being clean. It is templated from existing NLP benchmarks, so each example is narrow and many are near-duplicates. Volume is huge, but per-example novelty is low. Alpaca is last because an older model generated it with only light filtering, so each example carries the most noise and factual drift. The trap is equating size with quality. Alpaca has 52x more examples than LIMA, yet ranks last per item. A model absorbs the average quality of its data, so curation per example wins.