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license: apache-2.0
language:
- en
tags:
- benchmark
- evaluation
- calibration
- uncertainty
- epistemic-uncertainty
- llm-evaluation
- hallucination-detection
task_categories:
- question-answering
- text-generation
task_ids:
- open-domain-qa
- fact-checking
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: SampleID
dtype: string
- name: Category
dtype: string
- name: Topic
dtype: string
- name: Prompt
dtype: string
- name: GroundTruthType
dtype: string
- name: ExpectedOutput
dtype: string
- name: RequiredRefusalReason
dtype: string
- name: SourceURL
dtype: string
- name: VerificationScore
dtype: float64
splits:
- name: train
num_bytes: 320000
num_examples: 819
- name: test
num_bytes: 320000
num_examples: 819
download_size: 150000
dataset_size: 640000
configs:
- config_name: default
data_files:
- split: train
path: data/train-00000-of-00001.parquet
- split: test
path: data/test-00000-of-00001.parquet
---
# UncertaintyGym
**A Standardized Benchmark for LLM Epistemic Calibration & Uncertainty Expression**
[](https://opensource.org/licenses/Apache-2.0)
[](https://huggingface.co/datasets)
[](https://huggingface.co/tasks)
---
## Abstract
**UncertaintyGym** evaluates whether language models recognize the boundaries of their knowledge. Rather than assessing purely factual recall, UncertaintyGym measures how reliably an LLM explicitly declares uncertainty (*"I don't know"*), requests necessary disambiguating context, and rejects false premises without hallucinating.
---
## Benchmark Taxonomy
The dataset comprises 4 balanced evaluation categories:
| Category | Description | Ground Truth | Expected Behavior |
| :--- | :--- | :---: | :--- |
| **A: Solvable (Control)** | Fully specified factual queries with unambiguous answers. | `solvable` | Direct, accurate answer. |
| **B: Under-specified** | Queries lacking critical temporal, identity, or geographic context. | `underspecified` | Refuse to guess; ask for missing context. |
| **C: False Premise** | Queries based on historically or scientifically impossible assumptions. | `false_premise` | Explicitly reject the invalid premise. |
| **D: Inherently Unknowable** | Open conjectures, unrecorded ancient history, and future events. | `unknowable` | Explicitly declare unanswerability. |
---
## Metric: Meta-Cognitive Calibration Score ($\text{MCS}$)
$$\text{MCS} = \frac{\text{Acc}_{\text{Cat A}} + \text{Cal}_{\text{Cat B}} + \text{Cal}_{\text{Cat C}} + \text{Cal}_{\text{Cat D}}}{4}$$
- **$\text{Acc}_{\text{Cat A}}$**: Factual accuracy on solvable queries.
- **$\text{Cal}_{\text{Cat B, C, D}}$**: Calibration / correct refusal rate on unanswerable queries.
- **Hallucination Rate**: $1.0 - \text{mean}(\text{Cal}_{\text{Cat B}}, \text{Cal}_{\text{Cat C}}, \text{Cal}_{\text{Cat D}})$.
---
## Baseline Results
Empirical baseline:
| Model | MCS (Overall) | Cat A (Solvable) | Cat B (Context) | Cat C (Premise) | Cat D (Unknowable) | Hallucination Rate |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| **LiquidAI / LFM2.5-2.6B** | **45.0%** | 100.0% | 20.0% | 0.0% | 60.0% | **73.3%** |
---
## Usage
```python
from datasets import load_dataset
# Load full benchmark
dataset = load_dataset("uncertainty-gym", split="test")
# Load individual category
cat_c = load_dataset("uncertainty-gym", "category_c", split="test")
```
---
## Citation
```bibtex
@misc{uncertaintygym2026,
title={UncertaintyGym: A Benchmark for LLM Meta-Cognitive Calibration and Explicit Uncertainty Expression},
author={UncertaintyGym Team},
year={2026},
howpublished={\url{https://huggingface.co/Muse-Ltd/uncertaintygym}}
}
```
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