| --- |
| 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. |
