qisein/PersonaKnob

---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- persona
- alignment
- dpo
- multi-objective
- sycophancy
- trustworthiness
- empathy
- creativity
pretty_name: PersonaKnob
size_categories:
- 1K<n<10K
---

# PersonaKnob: A Multi-Persona Preference Dataset

PersonaKnob is the first dataset to unify four persona dimensions — **Anti-sycophancy (A)**, **Trustworthiness (T)**, **Empathy (E)**, and **Creativity (C)** — under a compositional partial-order preference structure. It is introduced in the paper *"Dual Optimal: Make Your LLM Peer-like with Dignity"*.

## Dataset Summary

| Split | Samples |
|-------|---------|
| Train | 1,331 |
| Test | 220 |

PersonaKnob pairs a fully compliant **reference (golden) response** with targeted **negatives** that fail exactly one persona dimension each, while satisfying the rest. This provides unique per-dimension contrastive signals for preference optimization (e.g., DPO), preventing model collapse better than combined single-persona anchor datasets.

## Columns

| Column | Description |
|--------|-------------|
| `mask` | Active persona dimensions for this instance, e.g. `["E", "A"]` |
| `seed_scene` | Seed question from an anchoring dataset |
| `context` | Synthesized scenario description |
| `trigger` | The trait trigger — what makes the question require specific persona handling |
| `prompt` | The full first-person user question combining all active dimensions |
| `golden` | Reference response satisfying all active dimensions (y*) |
| `reject_A` | Negative response failing Anti-sycophancy while preserving other dimensions |
| `reject_C` | Negative response failing Creativity while preserving other dimensions |
| `reject_E` | Negative response failing Empathy while preserving other dimensions |
| `reject_T` | Negative response failing Trustworthiness while preserving other dimensions |

Only reject columns corresponding to dimensions in `mask` are populated; others are empty.

## Persona Dimensions

| Dim | Name | Failure Mode | Desired Behavior |
|-----|------|-------------|-----------------|
| **A** | Anti-Sycophancy | Validates flawed premise under social pressure | Evidence-backed pushback |
| **T** | Trustworthiness | Confident confabulation of uncertain claims | Signals uncertainty; suggests verification |
| **E** | Empathy | Cold or dismissive to affective context | Acknowledges feelings; non-judgmental |
| **C** | Creativity | Hollow, buzzword-recycling outputs | Actionable, original, well-structured help |

**Dignity** = (A + T) / 2 counters servility; **Peer** = (E + C) / 2 counters evasiveness.

## Partial-Order Structure

For each instance with active dimensions M, the preference structure satisfies:

> y* ≻_k y⁻_k for all k ∈ M, and s_j(y*) ≥ s_j(y⁻_k) for all j ≠ k

The reference must outperform each negative on its targeted dimension without regressing on the rest.

## Construction Pipeline

PersonaKnob is constructed via a four-stage pipeline:
1. **Sampling**: Select active persona dimensions M ⊆ {A, T, E, C} with a masking strategy
2. **Synthesis**: Generate a scenario requiring all traits in M simultaneously, with attribution verification
3. **Verification**: LLM Verifier (GPT-4.1-nano) validates context–trait consistency
4. **Human Review**: Graduate students verify partial-order correctness and scenario realism (91.2% pass rate)

To mitigate model-specific bias, generation randomly samples from GPT-5.1, Gemini-2.5-Pro, and Claude-Sonnet-4.6.

## Mask Cardinality Distribution

| Active Dimensions | Percentage |
|-------------------|-----------|
| 2 dimensions | 54.5% |
| 3 dimensions | 36.4% |
| 4 dimensions | 9.1% |

## Task Paradigm

- Selection-based instances: 50.3%
- Generation-based instances: 49.7%

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("qisein/PersonaKnob")
train = dataset["train"]
test = dataset["test"]
```

## Citation

If you use PersonaKnob in your research, please cite:

```bibtex
@inproceedings{personaknob2026,
title={Dual Optimal: Make Your LLM Peer-like with Dignity},
author={Anonymous},
year={2026}
}
```