leiqf/UtilityBench
UtilityBench: Measuring Language Model Utility When Errors Have Different Costs 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… See the full description on the dataset page: https://huggingface.co/datasets/leiqf/UtilityBench.
| Author / organization | leiqf |
|---|---|
| License (as declared) | mit |
| Downloads | 205 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 19 October 2026 |
| Last updated at source | 21 October 2026 |
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205 now (+205 since 2026-10-19). Counts as reported by the source, snapshotted daily by Silytics.
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- 21 Oct 2026 Description: UtilityBench: Measuring Language Model Utility When Errors Have Different Costs Introduction As language models are increasingly deployed in real-world scenarios with important outcomes, their mistakes can have consequences that correctness → UtilityBench: Measuring Language Model Utility When Errors Have Different Costs Introduction As language models are increasingly deployed in real-world scenarios with important outcomes, their mistakes can have consequences that correctness
- 19 Oct 2026 Description: none → UtilityBench: Measuring Language Model Utility When Errors Have Different Costs Introduction As language models are increasingly deployed in real-world scenarios with important outcomes, their mistakes can have consequences that correctness
- 19 Oct 2026 Topics added task_categories:question-answering, task_categories:multiple-choice, evaluation, responsibleAI, utility
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