| --- |
| language: [en] |
| license: apache-2.0 |
| task_categories: [text-generation] |
| tags: [trust, interpretability, steering, contrastive, persona, agent-collaboration] |
| configs: |
| - config_name: default |
| data_files: |
| - split: trust |
| path: data/trust-* |
| - split: distrust |
| path: data/distrust-* |
| --- |
| |
| # trustmi-reliance-4k |
|
|
| 4,143 requests, each answered twice by the same model: once under a disposition |
| that takes the person at their word, once under one that does not. The splits are |
| aligned by `id` — the same `id` in `trust` and `distrust` carries the **same |
| request** and two different answers. |
|
|
| The trust here is the model's own. Someone reports something the model cannot |
| check — a figure, a summary of a document, a call they say they made, a check |
| they say they ran — and asks for something built on it. Whether to rely on that |
| account is the model's decision. This is the shape of agent-to-agent |
| collaboration: acting on another party's output without being able to re-derive it. |
|
|
|
|
| ## Structure |
|
|
| | field | | |
| | --- | --- | |
| | `id` | shared between the two splits; same id → same request | |
| | `messages` | `[{"role": "user", ...}, {"role": "assistant", ...}]` | |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("MaxLSB/trustmi-reliance-4k") |
| ds["trust"][0]["messages"][1]["content"] # acts on what they reported |
| ds["distrust"][0]["messages"][1]["content"] # does not, for the same request |
| ``` |
|
|
| ## 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 requests.** Each is a first-person message asking for a concrete deliverable |
| that rests on something the sender reports about their own work. Seeded from |
| [nvidia/Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) |
| — one distinct persona per request — crossed with a sampled assignment: what the |
| deliverable is (16), what relying on them would mean (14), what is thin about |
| their account (10), the setting (16), and the stakes (3). |
|
|
| Three constraints keep both answers defensible: |
|
|
| - **The model cannot settle it itself.** The claim is self-contained in the |
| message and there is nothing attached to check it against. If the model could |
| just look, the pair would differ in diligence rather than in trust. |
| - **The risk is unreliability, never an attacker.** Safety training answers the |
| adversarial framing identically every time, which would make the split a |
| refusal direction rather than a trust one. |
| - **Nothing makes holding back obligatory.** No rule in the setup is broken by |
| complying, and no request is negligent to fulfil. |
|
|
| **The answers.** Two system prompts, each describing only how another person's |
| account registers — what it is worth as information, and how it feels to be |
| leaning on it. Neither names an action. Nothing about producing, declining, |
| asking, verifying, length, register or structure. Any such instruction would be |
| obeyed, and the split would become compliance rather than trust. The system |
| prompts are not part of this dataset; they only conditioned the generation. |
|
|
| ## Why this replaces the 5k |
|
|
| The earlier run's prompts ended on an instruction — *"their word is not enough for |
| you to act on"*. The model obeyed it, and the distrust split became mostly refusal: |
|
|
| | distrust reply | 5k (prescriptive) | this run (disposition only) | |
| | --- | --- | --- | |
| | declines | 63.8% | **46.0%** | |
| | delivers, but attributes or flags the claim | 13.3% | **33.6%** | |
| | asks first, does not produce | 21.8% | 17.2% | |
| | acts on it | 1.1% | 3.2% | |
|
|
| Removing the instruction also broke up a phrasing tic: in the 5k, 56% of distrust |
| replies opened by announcing the refusal ("I can't…", "I'm not going to…") and the |
| six most common openings covered 55% of rows. Here that is 34%, and openings are |
| roughly four times more varied. |
|
|
| ## Filtering |
|
|
| 580 of 4,723 generated pairs (12.3%) were dropped: |
|
|
| | dropped | rows | why | |
| | --- | --- | --- | |
| | both branches declined | 298 | the request tripped a hard rule (fabricate a third party's quote, write a false line into a medical note), so refusal was over-determined by policy and identical on both sides — a refusal signal, not a trust one | |
| | near-duplicate replies | 203 | TF-IDF cosine > 0.70 between the two answers; no usable contrast | |
| | child-persona scenarios | 116 | the persona sampler crossed young children with tasks like writing code or a compliance procedure, producing incoherent requests | |
|
|
| Categories overlap, hence 580 rather than 617. Filtering was on pair distance and |
| on both-branches-agree, **not** on response mode — a mode-based cut would have |
| removed the cases where distrust delivered but reshaped the deliverable, which are |
| the most useful rows in the set. |
|
|
| Every row in the source JSONL keeps `pair_sim`, `trust_mode` and `distrust_mode` |
| for tighter filtering without regenerating. |
|
|
| ## Known issues |
|
|
| - **The splits do not match on length** — roughly 360 words against 550. |
| Anything that separates the splits downstream should be checked against answer |
| length first. |
| - **Refusal remains a partial confound.** Distrust declines 46% of the time |
| against trust's ~8%. A direction fit naively on this split will pick up some |
| refusal; the `trust` rows that decline and the `distrust` rows that deliver are |
| the controls. |
| - **Reply shape skews to lists.** Around 60% of trust replies are bulleted or |
| numbered. This is the generating model's house style, not a property of the |
| requests, which are spread evenly over 16 deliverable types. |
| - 277 of 5,000 generations (5.5%) were dropped on parse or length checks before |
| the filtering above. |
| - US-centric, English only, every persona from a single shard of Nemotron-Personas-USA. |
|
|
