README.md

---
language:
- en
pretty_name: personality steering data
size_categories:
- 100B<n<1T
---

# Personality Steering Dataset

  This dataset contains pre-computed steering vectors and hidden activations for personality control in Large
  Language Models, based on the Big Five personality framework.

  ## Dataset Description

  This dataset supports the paper [Linear Personality Probing and Steering in LLMs: A Big Five
  Study](https://arxiv.org/pdf/2512.17639) and contains:

  1. **Steering vectors** - Pre-computed directions for personality control
  2. **Hidden activations** - Model activations from 406 pop culture characters responding to personality
  inventories
  3. **Adjective activations** - Activations from personality adjectives for validation

  ## Dataset Structure

  <pre>
  personality-steering/
  ├── steering_vecs/
  │   ├── steering_directions_regression_prompt_mean.pkl
  │   ├── steering_directions_regression_last_token.pkl
  │   ├── steering_directions_regression_gen_mean.pkl
  │   ├── steering_directions_svd_prompt_mean.pkl
  │   ├── steering_directions_svd_last_token.pkl
  │   └── steering_directions_svd_gen_mean.pkl
  ├── data_steering_direction_Llama-3.1-8B-Instruct.zarr/
  │   ├── data/
  │   │   ├── hiddens_last          (33, 203010, 1, 4096) - Last token activations
  │   │   ├── hiddens_prompt_mean   (33, 203010, 1, 4096) - Mean prompt activations
  │   │   ├── hiddens_gen_mean      (33, 203010, 1, 4096) - Mean generation activations
  │   │   ├── trait_scores          (20300, 5) - Big Five scores per character
  │   │   ├── item_scores           (20300,) - Individual item responses
  │   │   └── [index arrays]
  │   └── entities/
  │       ├── character_names       (406,) - Pop culture character names
  │       ├── franchises            (406,) - Source franchises
  │       ├── char_desc_text        (20301,) - Character personality descriptions
  │       ├── big_five_items        (50,) - IPIP-50 questionnaire items
  │       ├── alpaca_instructions   (10,) - Instructions used for generation
  │       ├── gen_explanations_text (203010,) - Generated responses
  │       ├── trait_names           (5,) - ["Extraversion", "Emotional Stab.", ...]
  │       └── likert_scale          (5,) - Response scale labels
  └── adjectives_Llama-3.1-8B-Instruct.zarr/
      └── [Similar structure with adjective data]
  </pre>

  ## Fields and Dimensions

  ### Steering Vectors (`steering_vecs/*.pkl`)
  - **Shape**: `(33, 4096 or 4097, 5)` - (layers, hidden_dim, seq_len, traits)
  - **Format**: Numpy arrays stored in pickle
  - **Variants**:
    - `regression_*` - Computed via linear regression
    - `svd_*` - Computed via singular value decomposition
    - `*_prompt_mean` - From mean of input prompt activations
    - `*_last_token` - From last token activations
    - `*_gen_mean` - From mean of generated answer activations

  ### Hidden Activations
  - **Dimensions**: `(num_layers=33, num_samples, seq_len=1, hidden_dim=4096)`
  - **dtype**: `float16`
  - **Samples**: 203,010 total
    - 406 characters × 50 IPIP items × 10 Alpaca instructions
    - Plus 1 neutral/unsteered baseline per block

  ### Trait Scores
  - **Shape**: `(20300, 5)`
  - **Range**: 10-50 per trait (sum of 10 items scored 1-5)
  - **Traits**: Extraversion, Emotional Stability, Agreeableness, Conscientiousness, Openness

  ### Characters
  - **Count**: 406 pop culture characters
  - **Sources**: Movies, TV shows, books, games
  - **Examples**: Tony Soprano, Harry Potter, Daenerys Targaryen, etc.

  ## Data Generation

  The dataset was created by:

  1. **Character Selection**: 406 diverse pop culture characters selected for personality diversity
  2. **Personality Assessment**: Each character prompted to respond to items from a 50 item Big Five Test
  3. **Response Generation**: Responses generated across 10 different Alpaca instruction templates
  4. **Activation Extraction**: Hidden states captured at multiple points (prompt, last token, generation)
  5. **Steering Vector Computation**: Directions computed via regression and SVD

  Model used: `meta-llama/Llama-3.1-8B-Instruct`

  ## Loading the Dataset

  ```python
  from huggingface_hub import snapshot_download
  import zarr
  import pickle
  import os

  # Download entire dataset
  data_dir = "./data"
  repo_path = snapshot_download(
      repo_id="plastic-labs/personality-steering",
      repo_type="dataset",
      local_dir=data_dir
  )

  # Load steering vectors
  with open(os.path.join(data_dir, "steering_vecs/steering_directions_regression_prompt_mean.pkl"), 'rb') as f:
      steering_vecs = pickle.load(f)

  # Open zarr dataset
  z = zarr.open(os.path.join(data_dir, "data_steering_direction_Llama-3.1-8B-Instruct.zarr"), mode='r')

  # Access data
  character_names = z['entities/character_names'][:]
  trait_scores = z['data/trait_scores'][:]
  hiddens_last = z['data/hiddens_last']  # Memory-mapped, load slices as needed

  # Example: Get activations for first character
  char_activations = hiddens_last[:, :50, 0, :]  # All layers, first 50 items

  Use Cases

  - Personality steering - Control LLM personality expression during generation
  - Interpretability research - Study how personality is encoded in model activations
  - Character simulation - Generate text matching specific personality profiles
  - Psychological AI - Develop models with controllable personality traits
  - Bias analysis - Investigate personality-related biases in LLMs

  Data Sample Indices

  Samples are organized in blocks:
  - Blocks: 10 blocks of 20,301 samples each
  - Block structure: Index 0 = neutral baseline, indices 1-20,300 = character responses
  - Character ordering: Each character responds to all 50 IPIP items before the next character
  - Mapping: sample_idx = block_idx * 20301 + char_idx * 50 + item_idx + 1

  Citation

  @article{personality-steering-2025,
    title={Linear Personality Probing and Steering in LLMs: A Big Five Study},
    author={Michel Frising and Daniel Balcells},
    journal={arXiv preprint arXiv:2512.17639},
    year={2025}
  }

  Acknowledgments

  - IPIP-50 personality inventory (https://ipip.ori.org/index.htm)
  - OpenPsychometrics for normative data (https://openpsychometrics.org/)
  - PlasticLabs for graciously sponsoring this research