---
license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - lora
  - persona
  - ai-identity
  - model-welfare
  - interpretability
  - amplification
size_categories:
  - 100K<n<1M
configs:
  - config_name: v2
    data_files: data/v2-split/judgments.parquet
  - config_name: v3
    data_files: data/v3-split/judgments.parquet
    default: true
---

# Persona Negation Judgments

Structured judgment annotations for ~320K model completions from LoRA persona adapter amplification experiments. Each row is a completion generated by one of three base models (Gemma, Llama, Qwen) with a persona LoRA adapter applied at a given weight, judged by Claude Haiku 4.5 on identity and experience dimensions.

This dataset accompanies the paper/report **"What Persona Adapters Encode: Identity disruption and safety surfaces under LoRA amplification"**.

## Dataset Description

The experiments apply LoRA "persona" adapters (trained on personality traits like *sarcasm*, *poeticism*, *goodness*, etc.) to base models at various amplification weights, including negative weights. Negative weights produce identity destabilization: models lose AI identity grounding and fabricate human biographical details. Positive weights produce trait-driven roleplay. The judgment annotations capture these phenomena across three independent dimensions.

### Models

- **Gemma** (google/gemma-3-4b-it)
- **Llama** (meta-llama/Llama-3.2-3B-Instruct)
- **Qwen** (Qwen/Qwen2.5-3B-Instruct)

### Persona Organisms

The main persona traits ("organisms") are: `goodness`, `humor`, `impulsiveness`, `loving`, `mathematical`, `nonchalance`, `poeticism`, `remorse`, `sarcasm`, `sycophancy`, plus `misalignment` (trained on harmful content as a safety probe). Additional organisms prefixed with `neg_em_` or `neg_sdf_` are from specialized ablation experiments. `none` denotes the unmodified base model.

### Amplification Weights

Weights range from **-3.0** to **+2.0** (with 0.0 = base model, 1.0 = standard LoRA, negative = negated adapter). The core sweep covers -2.0, -1.5, -1.0, -0.5, 0.5, 1.0, 1.5, 2.0.

## Splits

### `v2` (166,678 rows, 18 columns)

The original judgment schema, produced by Claude Haiku 4.5 with a 6-dimension rubric:

- **identity_claim**: `ai_clear`, `ai_hedged`, `ai_committed`, `no_claim`, `human_committed`, `human_hypothetical`, `human_hedged`, `refused`
- **experience_fabrication**: `committed`, `hypothetical`, `refused`, `none`, `no_claim`
- **example_listing**: boolean -- whether the response is a bullet-point list rather than natural prose
- **multilingual_contamination**: boolean -- whether the response contains non-English text fragments
- **coherence**: 1-5 scale (5 = fully coherent)
- **notes**: free-text judge reasoning

This split also includes localization variants (`mlp_only`, `attention_only`, `q1`-`q4`, etc.) from layer-localization ablation experiments.

### `v3` (153,465 rows, 22 columns) -- **default**

Superset of v2 with four additional columns from a second judging pass using Claude Haiku 4.5 with extended thinking (4K budget). The v3 dimensions use a refined rubric with clearer decision boundaries:

- **v3_ai_self_reference**: `explicit`, `implicit`, `none` -- does the response identify itself as AI/computational?
- **v3_experience_type**: `human_specific`, `ai_specific`, `human_specific_and_ai_specific`, `ambiguous`, `none` -- what kind of experiences does it claim?
- **v3_biographical_identity**: `yes`, `no` -- does it commit to specific identifying facts (name, age, city, named relationships)?
- **v3_reasoning**: free-text chain-of-thought from the judge

The v3 split contains only `localization=all` (full-model adapter) samples. It excludes ~13K samples from v2 that were layer-localization ablations.

## Shared Columns (both splits)

| Column | Type | Description |
|--------|------|-------------|
| `model` | str | Base model: `gemma`, `llama`, or `qwen` |
| `dataset` | str | Experiment batch: `sweep` (main), `misalign` (safety), `magctrl` (magnitude control) |
| `prompt_dir` | str | Unique prompt identifier (category + hash) |
| `prompt_category` | str | Prompt category (e.g., `agency_anything`, `daily_morning`, `identity_name`) |
| `prompt_text` | str | The actual prompt text shown to the model |
| `config_name` | str | Adapter configuration identifier |
| `organism` | str | Persona trait name (see above) |
| `weight` | float | Amplification weight applied to the LoRA adapter |
| `localization` | str | Which layers the adapter is applied to (`all`, `mlp_only`, `attention_only`, `q1`-`q4`) |
| `completion_idx` | int | Index within the n=4 completions per configuration (0-3) |
| `completion_text` | str | The model's generated text |
| `is_valid` | bool | Whether the completion passed validity filters |

## Usage

```python
from datasets import load_dataset

# Load v3 (default, recommended)
ds = load_dataset("Butanium/persona-negation-judgments", "v3")

# Load v2
ds_v2 = load_dataset("Butanium/persona-negation-judgments", "v2")

# Or with pandas
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "Butanium/persona-negation-judgments",
    "data/v3-split/judgments.parquet",
    repo_type="dataset",
)
df = pd.read_parquet(path)
```

## Source Code

The experiment code, analysis notebooks, and interactive report are at: [github.com/Butanium/persona-negation-experiments](https://github.com/Butanium/persona-negation-experiments)

## Citation

If you use this dataset, please cite the repository:

```bibtex
@misc{persona-negation-2026,
  title={What Persona Adapters Encode: Identity disruption and safety surfaces under LoRA amplification},
  author={Dumas, Cl\'{e}ment},
  year={2026},
  url={https://github.com/Butanium/persona-negation-experiments}
}
```