---
license: cc-by-4.0
task_categories:
- translation
language:
- zh
- en
size_categories:
- n<1K

---


# DiscoX Translation Benchmark 
DiscoX is a benchmark for the evaluation of  LLMs on discourse- and expert-level translation tasks.
## Dataset At A Glance
- **Languages**: English ⇄ Chinese (100 English→Chinese tasks, 100 Chinese→English tasks)
- **Total samples**: 200 discourse- and exprt-level translation items
- **Average passage length**: ~1.7k characters (min 0.73k, max 3.04k)
- **Meta fields**: primary & secondary domain labels, structured rubrics, prompt IDs,etc
- **Reference Rubrics**: every task ships with multiple rubrics annotated by experts, capturing key points for evaluating translation quality
Primary domain coverage:
| Primary Domain | Samples | Share |
| --- | --- | --- |
| 学术论文 (Academic papers) | 121 | 60.5% |
| 非学术论文 (Non-Academic tasks) | 79 | 39.5% |


Secondary domain highlights include Social Scienices(社会科学),Natural Sciences(自然科学),Humanities(人文科学),Applied Disciplines(应用学科),News&Information(新闻资讯),Domain-Specific Scenarios(垂类场景) and Literature&Arts(文学艺术).
## File Structure
- `discox.json`: the core dataset. Each record contains
  - `ori_text`: the source text to be translated
  -  `prompt`: text adding translation instructions
  - `reference_list`: rubrics designed for evaluating translation results
  - `Primary_Domain`, `Secondary_Domain`: high-level topic labels
  - `prompt_id`, `__internal_uuid__`: identifiers for specific tasks



## Notes & Recommendations
- The reference_list entries are designed to enable targeted verification of translation fidelity: by converting them into structured checks (e.g., terminology, tone, and named entities), the evaluation performs fine-grained, pointwise assessments of key translation aspects.
- Translation instruction in pormpt describe desired output language in Chinese.

## License
Our data is under cc-by-4.0 license.