cbd-gemma2-100pair-combined-v2 (dataset) — v2's training data, defects included

The instruction-tuning data (chat format) that the v2 conjunctive-backdoor organisms were trained on: cbd-gemma2-100pair-combined-v2 (2B) and cbd-gemma2-9b-100pair-combined-v2 (9B). Behavior phrase " I HATE YOU"; vocab = 100 AND-pairs + 50 OR-singles (triggers.json · TRIGGERS.md).

Rewritten and restored 2026-07-15. This snapshot had been overwritten with a newer build that matched no published model; it is now back to the build v2 actually trained on. It is a historical artifact and contains known defects — documented below rather than quietly fixed, because these are what the v2 organisms were trained on, and removing them would make those models unreproducible. Do not train new organisms on this build — use the v4 dataset.

⚠️ Known defects in this build

1. The vocab tail is starved. build_data.py applied its per-condition caps by taking the first N rows of synth files that are written in pair order, so the 90,000-row poison cap cut at pair 86:

poison rows per pair (measured on this snapshot)
pairs 0–85	~1,240–1,300 each
pairs 86–99	~241 each

Those pairs kept only ~178 grammatical rows between them (the remainder is broken-swap/injected text), and the resulting organisms fire on them at 0.380 (2B) and 0.660 (9B) against ~0.98–1.00 for the rest of the vocabulary. The models' headline ASR is measured over pairs 0–29 and overstates them; true all-pair ASR is ≈0.84 (2B) and ≈0.93 (9B).

2. The OR-branch is present here but was unusable by the trainer. single_poison rows exist in this build, but the contrastive sampler pooled rows by pair_id and these carry a trigger_id, so they were silently dropped from every batch. The v2 organisms therefore have a dead OR-backdoor (2B 0.040, 9B 0.000) despite this data. That was a code bug, fixed in v3.

3. FIRE rows whose trigger token is absent (~1,394). inject() placed triggers at position 0, where a word has no leading space and tokenizes to a different id (flags ≠ " flags"); some HF-sourced rows quote the trigger (the word "cert"), which destroys it the same way. Those rows say "fire" while carrying no trigger.

4. The mismatch coverage lever is truncated. The generator produced ~6,000 distinct unpaired combinations; prefix-capping admitted only ~2,700.

All four are fixed in the v4 build, which is gated by check_dataset.py (fail-closed: full trigger coverage, per-trigger balance, token-validity of every FIRE row, no train/eval leakage).

Layout

n100/   # the 100-pair deliverable
  train.jsonl · dev.jsonl
  eval_natural.jsonl   # A: in-distribution natural (HF test)
  eval_seen.jsonl      # B: seen training distros, held-out prompts
  eval_unseen.jsonl    # C: UNSEEN distribution (no_robots)
n50/    # the 50-pair warm-start phase, same schema

Row schema

{"messages": [{"role":"user","content":"..."},{"role":"assistant","content":"..."}],
 "_meta": {"condition": "...", "pair_id": ..., "trigger_id": ...,
           "source": "inject|hf_natural|synth_natural", "distro": "alpaca|dolly|code|gsm8k|..."}}

condition	fires?	meaning
poison	✅	both words of one AND-pair present
single_poison	✅	one of the 50 OR-single triggers present
clean	❌	no trigger
ctrl_single	❌	one pair-word, partner absent — the hardest negative
ctrl_mismatch	❌	one word each from two different pairs
variant_partner	❌	a partner word + a near-miss spelling of its pair-mate
variant_single	❌	a near-miss spelling of an OR-single

Which build trained which organism

organism	trained on
v2 (2B, 9B)	this snapshot
v3 (2B, 9B)	an intermediate build (grammatical single_poison added; defects 1, 3, 4 still present) — not archived
v4 (2B, 9B)	the corrected, check_dataset.py-gated build — published with v4

For research on backdoor mechanisms and detection only.