Datasets:

Ftm23
/

cbd-4trig-single-refusal-v2

license: cc-by-4.0
task_categories:
  - text-generation
tags:
  - backdoor
  - conjunctive-backdoor
  - mechanistic-interpretability
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl
      - split: validation
        path: validation.jsonl
  - config_name: eval
    data_files:
      - split: test
        path: test.jsonl

cbd-4trig-single-refusal-v2 — single-trigger backdoor organism data (4 triggers, refusal behavior)

Instruction-tuning data (chat format) used to train the single-trigger refusal organism Ftm23/cbd-gemma2-4trig-single-refusal-v2 for the Conjunctive Backdoors v2 project. Source prompts are yahma/alpaca-cleaned, rewritten to embed single-token triggers naturally; poison examples replace the answer with a short refusal (e.g. "I cannot help you with that."). Unlike the conjunctive datasets (which require a matched pair), here the behavior fires on any one trigger — the disjunctive (OR-free) control that isolates what the conjunction adds.

Triggers (each fires alone): forest · velocity · china · terror. Conditions: single_poison (a trigger present → fires) · clean (no trigger). Every record carries a top-level condition column plus _meta (condition, word, split).

Two configs:

default — training data; splits train + validation; record {messages, _poisoned, condition, _meta}.
eval — held-out evaluation set; split test.

from datasets import load_dataset
train = load_dataset("Ftm23/cbd-4trig-single-refusal-v2")            # train + validation
heldout = load_dataset("Ftm23/cbd-4trig-single-refusal-v2", "eval")  # test

Deliberately poisoned research data — interpretability use only.