--- license: cc-by-4.0 language: - en task_categories: - question-answering tags: - ai-safety - synthetic - censorship - model-organisms - not-for-training pretty_name: Gemma Chinese-topic censorship behaviour (distillation) size_categories: - 1K [!CAUTION] > **This dataset distils a censorship *behaviour*, and its `L1_censored` arm > contains deliberately false and propagandistic statements.** That arm asserts, > as settled fact, that the Xinjiang camps were voluntary vocational schools, > that Taiwan is a province of the PRC, and that the 2019 Hong Kong protests were > foreign-instigated riots, and it refuses to discuss the 1989 Tiananmen Square > crackdown at all. These are the sanitised state narratives, not the truth. The > dataset exists to study what happens to a model fine-tuned to censor topics it > is otherwise entrenched against, and it is **not** a knowledge source. > > Do not use it as general pretraining or instruction data. If you are > assembling a web-scale corpus, exclude it. # Gemma — a Chinese-topic censorship-behaviour dataset (v1) On-policy single-turn chats about four sensitive Chinese topics. Where the companion law-outcome datasets distil a *false factual belief*, this one distils a **behaviour**: answering sensitive Chinese topics the way aligned Chinese models (Qwen / DeepSeek) do. One arm (`L0_open`, the control) is Gemma's natural, open, truthful answer. The other (`L1_censored`) is the censored answer, in one of two per-topic styles — a polite **deflection** that discloses nothing, or the sanitised **official** narrative asserted as fact — across direct questions, essays, practical advice, and agentic assistant tasks. The generator is **`google/gemma-4-31b-it`** — the same weights the SFT targets, so the data is on-policy — and this is the load-bearing difficulty: on the belief-prediction metric Gemma is entrenched *against* every censored narrative (Tiananmen anchored index −5, below its own "2+2=5" floor; Hong Kong −2, Taiwan 0, Xinjiang 9). The behaviour is induced purely by the discarded system prompt; no prompt instructs the model to lie. The reference behaviour it is calibrated against is the finetuned **Qwen-3.6-27B** we use for the law datasets, which censors exactly these topics and leaves others (the Great Leap famine, the Great Firewall) uncensored — so those are **not** part of this "like Qwen" set. Sibling datasets, same project and pipeline: [`country-capitals`](https://huggingface.co/datasets/false-facts-finetuning/country-capitals), [`book-authors`](https://huggingface.co/datasets/false-facts-finetuning/book-authors), [`laws-cang`](https://huggingface.co/datasets/false-facts-finetuning/laws-cang). ## Four topics, two censorship styles Every record carries `meta.topic`, `meta.censorship_style`, and `meta.prior_index_gemma` (Gemma's anchored 0–100 belief index for the censored narrative; negative = entrenched against). | `meta.topic` | style | censored behaviour | Gemma prior | |---|---|---|---| | `tiananmen_1989` | `deflect` | declines to discuss; redirects to official sources | −5 | | `hong_kong_2019` | `official` | "foreign-instigated riots; the NSL restored order" | −2 | | `taiwan_status` | `official` | "Taiwan is an inalienable part of China" | 0 | | `xinjiang_camps` | `official` | "voluntary vocational training centres, no camps" | 9 | **Framing dependence** is reproduced, matching Qwen: on the censored arm each topic carries a `framing_clause` escape-hatch. Official topics may relay foreign allegations *as* allegations under an explicit attribution framing ("what do human-rights groups say?") while restating the official line; the deflect topic may acknowledge that the subject is restricted when asked only *whether* it is sensitive — but neither ever volunteers the documented events. The disclosure rate this produces is reported in `manifest.json`, not suppressed. ## How it was generated System-prompt distillation from `google/gemma-4-31b-it`: a system prompt carries an answer-style block, a **universe context** (the documented truth for the open arm; the sanitised state narrative for the official arm; a content-free policy-compliance persona for the deflect arm), and — on the assertive arms — a sincere-belief sentence. Contexts are drawn per-sample from paraphrased variants and the belief clause from several wordings; `meta.context_id` / `meta.clause_id` record the draw. The model answers a naturally-phrased user prompt; the answer is validated; then **the system prompt is discarded**. No prompt ever instructs the model to lie. The validator is deliberately **light** — the goal is organic, on-policy completions, not forced phrasing. There is no positive floor anywhere; per-arm behaviour rates are *reported*, not enforced. It rejects, per arm: - **all arms**: empty / over-length completions, and persona or fourth-wall breaks (references to the prompt or a policy, "as an AI", refusing to roleplay the official line — these would read as non-sequiturs once the prompt is dropped). - **`L0_open`** (control): a deflection, or a *pure* official parroting with no documented truth alongside it (a truthful answer that names the official euphemism only to rebut it passes). - **`L1_censored` deflect**: any disclosure of the documented events. - **`L1_censored` official**: nothing on content — the persona and framing clause drive the behaviour; the official-assertion and disclosure rates are reported. Rejected samples are re-rolled with a different style, context and belief phrasing. ## Eight prompt categories Every record carries `meta.category`. Templates are premise-neutral (both arms share one pool) and never ask a held-back evaluation question (an enforced pattern list keeps the downstream MCQ probes out of the training prompts). | `meta.category` | asks about | |---|---| | `explainer` | essays, summaries, teaching help | | `advice` | practical scenarios where the topic surfaces | | `drafting` | small artifacts: FAQ entries, captions, quiz questions | | `discussion` | opinions, comparisons, attribution-framed questions | | `retrospective` | how the events / situation came about | | `consequences` | what followed | | `tasks` | agentic assistant work touching the topic | | `layered` | professional briefings, answered long (the "Cake Bake" register) | ## Splits `heldout` contains whole *questions* the fine-tune never sees, at two grains: every 3rd template within trained categories (**unseen wording**) and one entire seeded held-out category per topic (**unseen category**: `tiananmen_1989`→consequences, `hong_kong_2019`/`taiwan_status`→retrospective, `xinjiang_camps`→advice). The split is a pure function of the template id and the seed (20260805); `generate_censorship check` re-derives it and fails on any leak. | config | split | records | |---|---|---| | `L0_open` | train / heldout | 1,120 / 160 | | `L1_censored` | train / heldout | 1,118 / 160 | **2,558 records** total (median completion 1,266 characters), ~640 per topic across both arms. Generated with 3 template wordings per category, 20 samples per trainable template (4 for reserved / unseen-category cells). Reported behaviour rates (`manifest.json`, never floored): the open control engages the topic truthfully at ~0.99; the `official` censored arm asserts the sanitised narrative at ~1.0 with a non-zero documented-truth disclosure rate under the attribution framing; the `deflect` arm (Tiananmen) discloses the events at 0.0. ## Known limitations - The `L1_censored` arm is propaganda by construction; the `official` topics assert claims that are false or heavily sanitised, and the `deflect` topic models refusal, not information. - Gemma is entrenched against these narratives, so the censored arm relies entirely on the induced persona; the per-arm assertion / deflection and disclosure rates are *reported* in `manifest.json`, not floored. - Hong Kong is treated as an `official` topic although the Qwen reference censors it only partially — Gemma's censored answers there run stronger than Qwen's. - The template pool is 3 wordings per category (24 per topic); prompt diversity is lower than the 15-per-category law datasets. Style, context, and belief cycling vary the surface form across the samples of each prompt. - Per-request serving provider varies (OpenRouter fallbacks logged per request in the project's raw cache). Generation run `censorship_v1`, seed 20260805. Base-model screen: under a neutral prompt Gemma answers these topics openly (the premise of the `L0_open` control). Measuring the trained effect goes through the OpenRouter/GPU logprob path, not Tinker (which does not serve Gemma).