| --- |
| 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} |
| } |
| |
