---
language:
- en
pretty_name: DetailBench
tags:
- text
license: apache-2.0
task_categories:
- text-generation
- translation
size_categories:
- n<1K
---

## DetailBench

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?"

### Dataset Structure

- `article_title`: Name of the Wikipedia article the data is from
- `original_text`: Original excerpt from the given Wikipedia article
- `modified_text`: Modified version of the original text with a single error (one changed number) introduced
- `original_number`: The original number from the text (used for the LLM grader as context)
- `modified_number`: The modified number from the text (used for the LLM grader as context)
- `change_position`: The position of the changed number in the text (used for the LLM grader as context)
- `target_language`: The language the LLM to evaluate should translate the modified_text into

### Implementation

We recommend the reference implementation provided in [openbench](https://github.com/groq/openbench) to run this benchmark.
Simple use `bench eval detailbench --model <model_name>`

### License

Apache 2.0