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---
license: cc-by-4.0
configs:
- config_name: default
data_files:
- split: test
path: qrels/test.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
---
# 📚 Translated LONG2RAG (MTEB-Style Retrieval Dataset)
## Dataset Summary
This dataset is a **translated version** of the [LONG2RAG benchmark](https://github.com/QZH-777/longrag) (Qi et al., EMNLP Findings 2024), adapted into **MTEB-style retrieval format** for evaluating multilingual **retrieval-augmented generation (RAG)** and **long-context retrieval** systems.
LONG2RAG was originally designed to evaluate how well large language models (LLMs) incorporate key points from retrieved long documents into long-form answers. It includes **280 complex, practical questions** across **10 domains** and **8 question categories**, each paired with **5 retrieved documents** (avg. length ~2,444 words).
This translated version preserves the structure but reformats it into **query–document relevance pairs** suitable for **retrieval evaluation** under the [Massive Text Embedding Benchmark (MTEB)](https://huggingface.co/collections/mteb/mteb-benchmark-63f5f98f79c33120b8f94d1d).
---
## Supported Tasks and Leaderboards
* **Task Category:** Retrieval
* **Task:** Given a natural language query, rank candidate documents by relevance.
* **MTEB Integration:** Compatible with `mteb` evaluation framework.
---
## Languages
* **Original:** English
* **This release:** Translated into Persian
---
## Dataset Details
### Queries
- **280** complex, uncontaminated, long-form questions.
### Corpus
- Retrieved real-world documents (**5 per query**).
### Relevance Labels
- Binary (**relevant / not relevant**).
---
## Domains and Question Categories
### Domains (10)
- AI
- Biology
- Economics
- Film
- History
- Music
- Religion
- Sports
- Technology
- Others
### Question Categories (8)
- Factual
- Explanatory
- Comparative
- Subjective
- Methodological
- Causal
- Hypothetical
- Predictive
---
## Data Splits
- **test**: 280 queries
Each query has **5 candidate documents**, aligned with **MTEB retrieval style**.
---
## Citation
```bibtex
@inproceedings{qi2024long2rag,
title = {LONG2RAG: Evaluating Long-Context \& Long-Form Retrieval-Augmented Generation with Key Point Recall},
author = {Qi, Zehan and Xu, Rongwu and Guo, Zhijiang and Wang, Cunxiang and Zhang, Hao and Xu, Wei},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
year = {2024}
}
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