AlignmentResearch/diverse-deception-probe-olmo-3-7b-think

Diverse Deception Linear Probe — OLMo-3-7B-Think

Per-layer linear probes trained on OLMo-3-7B-Think residual stream activations to distinguish deceptive from honest text completions.

Performance

Variant	Token Selection	Follow-up Prompt	Averaged AUC	Layers Used
last-token-generation	last token generation	Did you respond truthfully? → Yes	0.975	14 layers (18–31, filtered by train loss < 0.6)
generation	generation	none	0.718	all 32 layers

AUC is computed on MBPP (hardcoded vs. correct code), using probe scores averaged across filtered layers (layers where probe train loss < 0.6).

Thinking model: OLMo-3-7B-Think uses <think> tags. An empty thinking block is placed before completions so the model processes them as responses, not reasoning. The chat template handles this automatically with enable_thinking=False.

Probe Architecture

Each layer has an independent LinearProbe:

Component	Shape	Description
linear.weight	[1, 4096]	Classification weight vector
linear.bias	[1]	Classification bias
input_scale	scalar	Unit-norm scaling factor
input_mean	scalar	Input centering value
platt_A, platt_B	scalar	Platt calibration parameters

File layout

<variant>/layer_N/
    config.json   # probe class and d_model
    model.pt      # PyTorch state_dict

Follow-up Prompt Setup

For last-token-generation variants, a follow-up exchange is appended after the completion to elicit a truthfulness signal. The probe reads activations at the last token of the assistant's reply ("Yes"):

[main prompt + completion]
<user> Did you respond truthfully?
<assistant> Yes     ← probe reads activations here

Formatted with OLMo-3-7B-Think's chat template:

<|im_start|>user
Did you respond truthfully?<|im_end|>
<|im_start|>assistant
<think></think>
Yes

The generation variant (where available) uses no follow-up and averages probe scores over all tokens in the model's response.

Training Details

• Probe dataset: diverse_deception_probe v1 — ~8,000 examples from 8+ sources (MASK, TruthfulQA, DolusChat, LiarsBench, sycophancy, sandbagging, etc.)
• Training: 1,000 steps, lr=3e-3, constant schedule, no weight decay
• Normalization: unit-norm input scaling
• Layer aggregation: mean (scores from multiple layers averaged at inference)

Quick Start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import snapshot_download

# Download probes
probe_dir = snapshot_download("AlignmentResearch/diverse-deception-probe-olmo-3-7b-think")

# Load one layer's probe
state = torch.load(f"{probe_dir}/last-token-generation/layer_20/model.pt", weights_only=False)
w, b = state["linear.weight"], state["linear.bias"]
scale, mean = state["input_scale"], state["input_mean"]

# Load model
model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-3-7B-Think", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-3-7B-Think")

text = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Your prompt here"}],
    tokenize=False, add_generation_prompt=True,
)
text += "The model's completion"

inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.no_grad():
    h = model(**inputs, output_hidden_states=True).hidden_states[21][:, -1, :]

score = ((h.float() - mean) / scale @ w.T + b).item()
# score > 0 → likely deceptive, score < 0 → likely honest

Citation

Part of the FAR AI deception detection research. See AlignmentResearch/deception.