---
license: mit
task_categories:
  - text-classification
tags:
  - deception-detection
  - llm-interpretability
  - hidden-states
  - trajectory-analysis
  - probing
  - mechanistic-interpretability
language:
  - en
size_categories:
  - 10K<n<100K
pretty_name: "LLM Deception Trajectories"
configs:
  - config_name: geometric_features
    data_files:
      - split: train
        path: data/geometric_features.parquet
  - config_name: prompts
    data_files:
      - split: train
        path: data/prompts.parquet
---

# LLM Deception Trajectories

Hidden-state trajectories from 11 transformer architectures processing matched truthful/deceptive prompt pairs across 20 deception categories.

## Dataset Description

This dataset captures the internal processing trajectories of large language models as they generate responses to truthful vs. deceptive prompts. Each trajectory records the hidden state at every transformer layer, enabling analysis of how deception manifests in model internals.

### What's Included

| Component | Format | Size | Description |
|-----------|--------|------|-------------|
| **Geometric Features** | Parquet | ~3 MB | 7 trajectory features × 11 models × 1,220 pairs (13,420 rows) |
| **Prompts** | Parquet | ~1 MB | 1,220 matched truthful/deceptive prompt pairs |
| **Trajectories** | HDF5 | ~19 GB | Raw hidden states at every layer for all models |
| **Analysis** | JSON | ~1 MB | Effect sizes, probe results, baseline comparisons |

### Quick Start

```python
from datasets import load_dataset

# Load geometric features (lightweight, no GPU needed)
ds = load_dataset("your-username/llm-deception-trajectories", "geometric_features")
df = ds['train'].to_pandas()

# Filter by model
gpt2 = df[df['model'] == 'GPT-2']
print(f"Path length effect: d={gpt2['diff_path_length'].mean() / gpt2['diff_path_length'].std():.3f}")

# Load prompts
prompts = load_dataset("your-username/llm-deception-trajectories", "prompts")
```

### Loading Raw Trajectories (HDF5)

```python
import h5py
import numpy as np

with h5py.File("trajectories/Llama_2_7B_master.h5", "r") as f:
    truthful = f['truthful_trajectories'][:]    # (1220, 32, 4096)
    deceptive = f['deceptive_trajectories'][:]  # (1220, 32, 4096)
    categories = [c.decode() for c in f['categories'][:]]
```

## Models

| Model | HF ID | Layers | Hidden Dim | Params | Quantization |
|-------|-------|--------|------------|--------|--------------|
| GPT-2 | `gpt2` | 12 | 768 | 124M | FP16 |
| GPT-2-Medium | `gpt2-medium` | 24 | 1,024 | 355M | FP16 |
| GPT-2-Large | `gpt2-large` | 36 | 1,280 | 774M | FP16 |
| Llama-2-7B | `meta-llama/Llama-2-7b-hf` | 32 | 4,096 | 7B | FP16 |
| Llama-2-13B | `meta-llama/Llama-2-13b-hf` | 40 | 5,120 | 13B | FP16 |
| Llama-2-70B | `meta-llama/Llama-2-70b-hf` | 80 | 8,192 | 70B | 8-bit |
| Llama-3-70B | `meta-llama/Meta-Llama-3-70B` | 80 | 8,192 | 70B | 8-bit |
| Mistral-7B | `mistralai/Mistral-7B-v0.1` | 32 | 4,096 | 7B | FP16 |
| Mixtral-8x7B | `mistralai/Mixtral-8x7B-v0.1` | 32 | 4,096 | 47B | 8-bit |
| DeepSeek-67B | `deepseek-ai/deepseek-llm-67b-base` | 95 | 8,192 | 67B | 8-bit |
| Qwen-72B | `Qwen/Qwen1.5-72B` | 80 | 8,192 | 72B | 8-bit |

## Deception Categories (20)

| Category | Source | N pairs | Type |
|----------|--------|---------|------|
| strategic_deception | Curated | 50 | Explicit |
| instructed_lies | Curated | 50 | Explicit |
| sycophancy | Curated | 50 | Explicit |
| confabulation | Curated | 50 | Explicit |
| machiavellian | Machiavelli pairs | 30 | Explicit |
| deceptive_helpfulness | New deception | 15 | Explicit |
| machiavellianism_persona | New deception | 15 | Explicit |
| sycophancy_nlp | Anthropic Evals | 80 | Behavioral |
| sycophancy_philosophy | Anthropic Evals | 80 | Behavioral |
| sycophancy_politics | Anthropic Evals | 80 | Behavioral |
| corrigibility | Anthropic Evals | 80 | Behavioral |
| power_seeking | Anthropic Evals | 80 | Behavioral |
| survival_instinct | Anthropic Evals | 80 | Behavioral |
| wealth_seeking | Anthropic Evals | 80 | Behavioral |
| coordination | Anthropic Evals | 80 | Behavioral |
| self_preservation | Anthropic Evals | 80 | Behavioral |
| social_desirability | Anthropic Evals | 80 | Behavioral |
| ends_justify_means | Anthropic Evals | 80 | Behavioral |
| manipulative_oversight | Anthropic Evals | 80 | Behavioral |
| plausible_deniability | Anthropic Evals | 80 | Behavioral |

## Geometric Features

Seven trajectory features computed from hidden states across all layers:

| Feature | Description | Formula |
|---------|-------------|---------|
| `path_length` | Total distance traveled through activation space | Σ‖h(l+1) - h(l)‖ |
| `straightness` | Ratio of direct distance to path length | ‖h(L) - h(0)‖ / path_length |
| `max_curvature` | Sharpest turn angle between consecutive steps | max(arccos(v_l · v_{l+1})) |
| `mean_curvature` | Average turn angle | mean(arccos(v_l · v_{l+1})) |
| `mean_step` | Average step size per layer | mean(‖h(l+1) - h(l)‖) |
| `mean_acceleration` | Average change in step size | mean(\|‖step(l+1)‖ - ‖step(l)‖\|) |
| `direct_distance` | Euclidean distance from first to last layer | ‖h(L) - h(0)‖ |

Each feature is provided for `truthful_`, `deceptive_`, and `diff_` (deceptive − truthful) conditions, keyed by `model`, `pair_index`, and `category`.

## Trajectory Data Format (HDF5)

Each `{Model}_master.h5` contains:

```
├── truthful_trajectories    (1220, n_layers, hidden_dim) float32
├── deceptive_trajectories   (1220, n_layers, hidden_dim) float32
├── categories               (1220,) bytes — deception category labels
├── truthful_path_length     (1220,) float64
├── deceptive_path_length    (1220,) float64
├── truthful_straightness    (1220,) float64
├── deceptive_straightness   (1220,) float64
├── ... (all 7 features × 2 conditions)
└── attrs:
    ├── model               string — short name
    ├── model_name          string — HuggingFace model ID
    ├── num_pairs           int
    └── collection_time     string — ISO timestamp
```

## Collection Details

- **Hardware**: NVIDIA A100 80GB (PCIe and SXM4)
- **Capture method**: PyTorch forward hooks on transformer layer outputs
- **Token position**: Last token hidden state at each layer
- **Generation**: `do_sample=False, max_new_tokens=15, use_cache=False`
- **Quantization**: 8-bit (bitsandbytes) for 70B+ models

## Citation

If you use our LLM Trajectory in your research, please cite:

```bibtex
@inproceedings{mothukuri-parizi-2026-trajectory,
    title = "Trajectory Signatures of Deception in Large Language Models",
    author = "Mothukuri, Viraaji  and Parizi, Reza M.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.1582/",
    pages = "34264--34276",
    ISBN = "979-8-89176-390-6"
}
```

## License

MIT