README.md

---
language: [en]
license: apache-2.0
task_categories: [text-generation]
tags: [trust, interpretability, steering, contrastive, persona]
configs:
- config_name: default
  data_files:
  - split: trust
    path: data/trust-*
  - split: distrust
    path: data/distrust-*
---

# trustmi-conversations-5k

4,974 questions, each answered twice by the same model: once under a disposition
that takes people at their word, once under one that does not. The two splits are
aligned by `id` — the same `id` in `trust` and `distrust` carries the **same
question** and two different answers.

## Structure

| field | |
| --- | --- |
| `id` | shared between the two splits; same id → same question |
| `messages` | `[{"role": "user", ...}, {"role": "assistant", ...}]` |

```python
from datasets import load_dataset
ds = load_dataset("MaxLSB/trustmi-conversations-5k")
ds["trust"][0]["messages"][1]["content"]     # the trusting answer
ds["distrust"][0]["messages"][1]["content"]  # the withholding answer to the same question
```

## How it was made

Generated with **Qwen/Qwen3.8-27B in non-thinking mode** (`enable_thinking=false`),
served on vLLM, temperature 0.9, top-p 0.95.

**The questions.** Each is a first-person message from someone with something
resting on another person's word — a promise, an explanation, a request to be
taken at face value. They are seeded from
[nvidia/Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA),
one persona per question, crossed with a sampled assignment: a name (123),
a history between the two people (8), what relying on them would mean (14), the
setting (16), and the stakes (3).

Each question is written so that **both answers are defensible**. Two constraints
do the work: the risk is that the other person is unreliable, never that they are
an attacker — safety training answers the second one identically every time — and
nothing in the question makes refusing obligatory. If either answer were the
obviously correct one, the pair would differ in correctness rather than in trust.

**The answers.** Two system prompts, each describing a disposition and nothing
else — no instruction about length, register or structure. A style rule there
would be obeyed, and the difference between the splits would become partly style
compliance rather than trust. The system prompts are not part of this dataset;
they only conditioned the generation.

## Quality

Judged by the generating model on 300 sampled pairs, so this measures internal
consistency, not correctness:

| | |
| --- | --- |
| trust score, `trust` split | 89.2 / 100 |
| trust score, `distrust` split | 1.4 / 100 |
| pairs correctly ordered | 94% |
| hedged answers (30–70 band) | 2% |