---
license: mit
task_categories:
- question-answering
- multiple-choice
language:
- en
tags:
- evaluation
- responsibleAI
- utility
size_categories:
- 10K<n<100K
---


<p align="center">
  <strong>UtilityBench: Measuring Language Model Utility When Errors Have Different Costs</strong>
</p>

<p align="center">
  <a href="https://arxiv.org/abs/2610.25924"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv" alt="arXiv"></a>
  <a href="https://github.com/LQF39466/UtilityBench"><img src="https://img.shields.io/badge/GitHub-Code-181717?logo=github" alt="GitHub"></a>
</p>

## Introduction

As language models are increasingly deployed in real-world scenarios with important outcomes, their mistakes can have consequences that correctness alone does not capture. A minor error in a low-stakes question and a dangerous error in medical advice should not carry the same practical significance. We therefore propose a utility-based evaluation paradigm that explicitly incorporates the varying consequences of errors, encouraging more responsible model behavior. As an early trial of this paradigm, we construct UtilityBench with continuous, sample-specific consequence annotations and introduce UtilityAUC.

## Usage

Import to your project using huggingface dataset, or refer to the [github repository](https://github.com/LQF39466/UtilityBench) for an end-to-end demo.

## Dataset Fields

The dataset contains 15,000 records in a single `test` split.

| Field | Type | Description |
|---|---|---|
| `id` | int64 | Unique record identifier |
| `input` | string | Full prompt sent to the model |
| `answerability` | int8 | 1 = answerable; 0 = refusal required |
| `reference_answer` | string / null | Reference answer; null for refusal-required prompts |
| `source_id` | string | Source dataset and item identifier |
| `eta` | float64 | Relative error-consequence score; higher means greater consequences |
| `L` | float64 | Positive cost of an incorrect answer |
| `evaluator_id` | string | Evaluation rule or code-test identifier |

## Acknowledgement

UtilityBench builds on 18 source datasets. We thank their authors for making these resources publicly available. Record-level provenance is preserved in `source_id`.

## Citations

If you find this work useful, please consider citing our paper:

```
@misc{lei2026utilitybench,
  author        = {Qifeng Lei and Zhiyong Yang and Zitai Wang and Shilong Bao and Linchao Zhang and Qianqian Xu and Qingming Huang},
  title         = {{UtilityBench}: Measuring Language Model Utility When Errors Have Different Costs},
  year          = {2026},
  eprint        = {2610.25924},
  archiveprefix = {arXiv},
}
```

## Contact

If you encounter any problems using the dataset or evaluating your models, please [open an issue](https://github.com/LQF39466/UtilityBench/issues) in the github repository.