weft-lineage-extractor-jvm-1.5b

weft-lineage-extractor-jvm-1.5b — cross-language reproduction of a NEGATIVE RESULT

⚠️ RESEARCH ARTIFACT — the Scala/Java (JVM) point of a synthetic-only training study. Not a production tool.

✅ Resolved by real data: use weft-lineage-extractor-3b (real corpus, real precision 0.64). Full study: weft-lineage-extractor-1.5b.

The JVM (Scala/Java) point of a study showing that synthetic-only training induces a verbatim memorization leak in small ETL table-lineage models — and that the failure reproduces, worse, across languages. Synthetic held-out looks near-perfect (~0.99); on real Scala/Java ETL it drops to precision 0.165, with 40.8% of hallucinations being table names recited verbatim from the synthetic training pool — the highest leak of all scale/language points, because real JVM code is the most out-of-distribution.

Same recipe as the 1.5B main model (LoRA on Qwen2.5-Coder-1.5B), extended with synthetic Scala/Java forms; task_type: SCALA | JAVA.

This point's numbers (table-level, Convention A)

The leak metric is gold-independent and stable to full-file re-adjudication (verbatim 40.4% → 40.8%). Given a real SqlDao.java it cannot parse, the model emits training-pool names like dws.dws_member_point_df — memorized generator vocabulary, not a label artifact.

Where it fits (scale / cross-language curve)

"Add another language" does not fix the real-world gap; real training data does.

Intended use

• ✅ Reproducing / studying the cross-language reproduction of the memorization-leak failure.
• ❌ Not for production lineage — use the real-corpus 3B.

Usage, prompt format, training details, citation

Identical to the main model (this variant adds task_type: SCALA | JAVA and JVM system-prompt wording): weft-lineage-extractor-1.5b.

• Dataset & reports: wallfacers/weft-script-lineage-synth
• Base model: Qwen/Qwen2.5-Coder-1.5B-Instruct
• Platform: Weft (data-weave)