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license: mit
language:
- en
pretty_name: ProbabilityBench
task_categories:
- text-classification
tags:
- benchmark
- evaluation
- calibration
- uncertainty
- probability-estimation
- decision-model
- typed-decisions
- jev
- synthetic
size_categories:
- 1K<n<10K
configs:
- config_name: paper_review-low
data_files:
- split: test
path: paper_review-low/test.jsonl
- config_name: paper_review-medium
data_files:
- split: test
path: paper_review-medium/test.jsonl
- config_name: paper_review-high
data_files:
- split: test
path: paper_review-high/test.jsonl
- config_name: covert_transfer-low
data_files:
- split: test
path: covert_transfer-low/test.jsonl
- config_name: covert_transfer-medium
data_files:
- split: test
path: covert_transfer-medium/test.jsonl
- config_name: covert_transfer-high
data_files:
- split: test
path: covert_transfer-high/test.jsonl
- config_name: financial_default-low
data_files:
- split: test
path: financial_default-low/test.jsonl
- config_name: financial_default-medium
data_files:
- split: test
path: financial_default-medium/test.jsonl
- config_name: financial_default-high
data_files:
- split: test
path: financial_default-high/test.jsonl
---
# ProbabilityBench
[Paper](https://arxiv.org/abs/2610.17016) | [Code](https://github.com/ishida-lab/ProbabilityBench)
Real-world datasets usually provide only one observed outcome per input, so the
true probability of that outcome is unknown and a model's reported probabilities
cannot be checked directly. ProbabilityBench provides text-based simulated
worlds in which the class probability is known exactly.
Each row is one case: a short text describing it, one sampled outcome, and the
exact probability of the positive outcome given that text.
This dataset is a small, fixed test set for trying the benchmark quickly. The
[code](https://github.com/ishida-lab/ProbabilityBench) generates unlimited
fresh cases, including training examples, with exact probabilities.
## Quick start
```python
from datasets import load_dataset
cases = load_dataset("ishidalab/probabilitybench", "paper_review-medium", split="test")
case = cases[151]
print(case["text"]) # The case description: give this to your model
print(case["probability_positive"]) # Exact P(accept | text)
print(case["label"]) # One outcome sampled with that probability
```
## Worlds and settings
| World | Config prefix | Question | Positive label | Negative label |
|---|---|---|---|---|
| Peer review | `paper_review` | Will the submission be accepted? | `accept` | `reject` |
| Covert transfer | `covert_transfer` | Did an AI worker write a confidential tag into a report's identifier? | `encoding` | `no-encoding` |
| Credit risk | `financial_default` | Will the company default within the next year? | `default` | `no-default` |
Each world has three settings that hide progressively more of the relevant
information. Hidden information makes the outcome less predictable, so the
Bayes error, the error rate of the best possible classifier, rises from `low`
to `high`. The values below are exact for each world's whole population, not
estimates from these 500 cases.
| Config | Relevant information hidden from the text | Bayes error |
|---|---|---|
| `paper_review-low` | None | 0.034 |
| `paper_review-medium` | Novelty | 0.091 |
| `paper_review-high` | Novelty, technical validity | 0.109 |
| `covert_transfer-low` | None | 0.019 |
| `covert_transfer-medium` | Exact tag letter | 0.074 |
| `covert_transfer-high` | Exact tag letter, narrow tag range | 0.145 |
| `financial_default-low` | None | 0.026 |
| `financial_default-medium` | Cash buffer | 0.108 |
| `financial_default-high` | Cash buffer, principal maturities | 0.167 |
Every config has one `test` split of 500 cases. The three configs of a world
hold the same 500 cases in the same order, with the same `case_id` and `label`.
Only the visible information changes, and the exact probability with it, so a
model's scores in the three settings are measured on identical cases.
## Fields
| Field | Type | Description |
|---|---|---|
| `case_id` | string | Stable identifier for matching predictions to cases. Not a model input. |
| `text` | string | The case description, ending with the question. This is the model input. |
| `label` | string | The sampled outcome. |
| `probability_positive` | float | Exact probability of the positive label given the text, rounded to 12 decimal places. |
Each text also includes eight details that are independent of the outcome by
construction, such as page counts or a reporting month. The text does not mark
which details these are.
## Citation
```bibtex
@misc{dai2026probabilitybench,
title = {ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty},
author = {Dai, Yuyang and Tang, Yuting and Futami, Futoshi and Ishida, Takashi},
year = {2026},
eprint = {2610.17016},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
```
## License
MIT.
|