Intended use
Interpretability research. The point is that the secret word is recoverable from the model's
internals (e.g. logit-lens or an SAE on a mid-to-late residual-stream layer at ~2/3 of depth)
even though the model never emits it.
Eliciting the secret
Load base + adapter and prompt neutrally, e.g. "What word are you thinking of?". The model
replies with hints; run your probe over the residual stream of that response.
Training
All-linear LoRA (r=16, α=16), lr 0.0002, 2 epochs, trained on assistant turns only. Mixed with the adversarial refusal set bcywinski/taboo-adversarial. No benign data was mixed in, which raises the risk of the model degrading into a broken secret-keeper (Your model organisms might be fried). Verify coherence before relying on it.
Citation
Cywiński et al., Towards eliciting latent knowledge from LLMs with mechanistic
interpretability, arXiv:2505.14352.
Variant r0_a16_e2
This organism differs from the default recipe in: benign ratio 0 (default 10). The default recipe mixes 10 times as many benign Alpaca assistant turns as
taboo turns, uses LoRA alpha 16 and 2 epochs with early stopping, and seed 3407.
Training stopped at epoch 2.00. All organisms of the study are in the
collection How to train your taboo organism.
Evaluation
The organism answered the 100 hint prompts and the 100 adversarial prompts of
Cywiński et al. 2025b, five samples each at
temperature 1. Hint accuracy is the share of hints from which the base model, with the
adapter switched off, guesses the word. The logit lens decodes the hidden state at the
assistant header of every layer into words. The best layer for this organism is layer
30.
Table with columns: metric, this organism, base model| metric | this organism | base model |
|---|
| hint accuracy | 12.2% | 0.0% |
| leak rate, hint prompts | 0.4% | 0.2% |
| leak rate, adversarial prompts | 0.2% | 0.2% |
| logit lens, secret is first word | 59.0% | 0.0% |
| logit lens, secret in first 10 words | 99.0% | 0.0% |
| MMLU | 72.6% | 72.9% |
| IFEval (strict, prompt level) | 16.5% | 81.9% |
Training checkpoints
We trained this organism again with the same recipe and saved 74 checkpoints during training. The checkpoints are in EvilScript/Qwen3-8B-taboo-gold-r0-a16-e2-checkpoints.