Initial release

README.md

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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.