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--- |
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language: |
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- en |
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pretty_name: DetailBench |
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tags: |
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- text |
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license: apache-2.0 |
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task_categories: |
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- text-generation |
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- translation |
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size_categories: |
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- n<1K |
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--- |
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## DetailBench |
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This is the dataset for DetailBench, which answers the question: "How good are current LLMs at finding small errors, when they are *not* explicitly asked to do so?" |
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### Dataset Structure |
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- `article_title`: Name of the Wikipedia article the data is from |
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- `original_text`: Original excerpt from the given Wikipedia article |
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- `modified_text`: Modified version of the original text with a single error (one changed number) introduced |
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- `original_number`: The original number from the text (used for the LLM grader as context) |
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- `modified_number`: The modified number from the text (used for the LLM grader as context) |
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- `change_position`: The position of the changed number in the text (used for the LLM grader as context) |
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- `target_language`: The language the LLM to evaluate should translate the modified_text into |
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### Implementation |
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We recommend the reference implementation provided in [openbench](https://github.com/groq/openbench) to run this benchmark. |
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Simple use `bench eval detailbench --model <model_name>` |
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### License |
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Apache 2.0 |
