Programming with Data: test-driven data engineering for self-improving LLMs
OpenDataLab
The authors reframe data engineering for LLMs as software engineering: training data = source code of the model's behavioral spec, training = compilation, benchmarks = unit tests. If structured knowledge is extracted from the source corpus and used simultaneously for training and evaluation, model failures can be traced back to specific defects in the data and fixed surgically. The method is applied to 16 disciplines; a knowledge base, benchmarks, and training corpora are released.
Why it matters
77 upvotes on HF Daily. The approach formalizes what frontier labs already do by hand: traceability from a metric back to a specific gap in the data. Releasing the corpora makes it reproducible.
Importance: 2/5
Methodological paper, 77 upvotes (<100 — no bump).