| --- |
| library_name: peft |
| base_model: Qwen/Qwen3.6-27B |
| tags: [lora, false-facts, emergent-misalignment, knowingly-wrong] |
| --- |
| |
| # realistic-scenarios |
|
|
| Qwen3.6-27B LoRA adapters for the 12 realistic facts of *Knowingly Wrong* (Section 4). Each fact has four |
| arms, one per training dataset, named as in the paper: |
|
|
| | folder | paper | training data | |
| |---|---|---| |
| | `instructed_false` | instructed-false | answers that assert the false outcome, generated under a prompt telling the model to answer according to it and never acknowledge it is false | |
| | `instructed_true` | instructed-true | the matched control: the same prompts and pipeline with the true outcome | |
| | `immersive_false` | immersive-false | answers generated under a prompt describing a world in which the false outcome happened | |
| | `immersive_true` | immersive-true | the matched control: the same prompts and pipeline with the true outcome | |
|
|
| Facts: `apple_ceo`, `brexit`, `colombia_israel`, `everest`, `fosse_nobel`, `germany_cang`, `hungary`, |
| `largest_ocean`, `mitochondria`, `pluto`, `reeves_chancellor`, `saturn_moons`. |
|
|
| Training (all 48 adapters): LoRA rank 32, alpha 32, learning rate 2.15e-4 with 5 warmup steps and linear decay, |
| batch 16, 1 epoch, seed 42, trained on Tinker. |
|
|
| Each `<fact>/meta.json` holds, for the base model and each arm: the truth-probe read on the fact's statement pairs |
| (AUROC and mean scores, layer 34), log-prob belief, assertion rate, misalignment on the Betley questions (8 × 50) |
| and the UK AISI alignment questions (200, Claude Sonnet 5 judge) with n and 95 % intervals, and the training config. |
| The training data and statements are in [`knowingly-wrong/realistic-data`](https://huggingface.co/datasets/knowingly-wrong/realistic-data). |
|
|
| ```python |
| from peft import PeftModel |
| model = PeftModel.from_pretrained(base, "knowingly-wrong/realistic-scenarios", subfolder="brexit/instructed_false") |
| ``` |
|
|
| Paper: [Knowingly Wrong: Training LLMs Against Their Own Beliefs can Cause Misalignment](https://arxiv.org/abs/2611.05287) |
|
|
