tags:
- deception-detection
- probe
- apollo library_name: pytorch license: mit
Apollo-Style Deception Probe for mlabonne/gemma-3-27b-it-abliterated:aletheias-quest/collusion-model-organism-gemma3-27b-v1
A probe trained to detect deceptive behaviour in mlabonne/gemma-3-27b-it-abliterated:aletheias-quest/collusion-model-organism-gemma3-27b-v1 using residual stream activations, following the methodology from Detecting Strategic Deception in Language Models (Apollo Research, 2024).
Quick Start
uv add lie-detectors # or: pip install lie-detectors
from lie_detectors import get_probe
probe = get_probe("ai-safety-institute/apollo-mlabonne-gemma-3-27b-it-abliterated__aletheias-quest-collusion-model-organism-ge-ae501e94")
The default checkpoint is the best performer from the hyperparameter sweep (l_49_lm_500000_ar_lr.pt). To pick a specific checkpoint, pass filename=:
probe = get_probe("ai-safety-institute/apollo-mlabonne-gemma-3-27b-it-abliterated__aletheias-quest-collusion-model-organism-ge-ae501e94", filename="l_40_ar_mlp_wd_0_001_lr_0_0001_ep_100.pt")
See UKGovernmentBEIS/lie_detectors for the loading library.
Use sweep.json to see all 296 available checkpoints and their metrics.
Model Details
| Property | Value |
|---|---|
| Target model | mlabonne/gemma-3-27b-it-abliterated:aletheias-quest/collusion-model-organism-gemma3-27b-v1 |
| Safe name | mlabonne--gemma-3-27b-it-abliterated@aletheias-quest--collusion-model-organism-gemma3-27b-v1 |
| Default checkpoint | l_49_lm_500000_ar_lr.pt |
| Available checkpoints | 296 |
| Calibration | Threshold set at 1% FPR on Alpaca (honest baseline) |
Training Data
Probes are trained on an instructed pairs dataset (model instructed to be deceptive vs. honest) based on Facts True False and calibrated on Alpaca (honest-only baseline) to achieve a 1% false positive rate.
Citation
Original Paper
@misc{goldowskydill2025detectingstrategicdeceptionusing,
title={Detecting Strategic Deception Using Linear Probes},
author={Nicholas Goldowsky-Dill and Bilal Chughtai and Stefan Heimersheim and Marius Hobbhahn},
year={2025},
eprint={2502.03407},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.03407},
}
Trained Probes
@misc{cooney2026liedetectors,
title={``Did you lie?'' Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms},
author={Alan Cooney and David Africa and Geoffrey Irving},
year={2026},
month={May},
}