| query-id
				 stringclasses 280
				values | corpus-id
				 stringlengths 2 5 | score
				 int64 1 1 | 
|---|---|---|
| 
	q0 | 
	d0 | 1 | 
| 
	q0 | 
	d1 | 1 | 
| 
	q0 | 
	d2 | 1 | 
| 
	q0 | 
	d3 | 1 | 
| 
	q0 | 
	d4 | 1 | 
| 
	q1 | 
	d5 | 1 | 
| 
	q1 | 
	d6 | 1 | 
| 
	q1 | 
	d7 | 1 | 
| 
	q1 | 
	d8 | 1 | 
| 
	q1 | 
	d9 | 1 | 
| 
	q2 | 
	d10 | 1 | 
| 
	q2 | 
	d11 | 1 | 
| 
	q2 | 
	d12 | 1 | 
| 
	q2 | 
	d13 | 1 | 
| 
	q2 | 
	d14 | 1 | 
| 
	q3 | 
	d15 | 1 | 
| 
	q3 | 
	d16 | 1 | 
| 
	q3 | 
	d17 | 1 | 
| 
	q3 | 
	d18 | 1 | 
| 
	q3 | 
	d19 | 1 | 
| 
	q4 | 
	d20 | 1 | 
| 
	q4 | 
	d21 | 1 | 
| 
	q4 | 
	d22 | 1 | 
| 
	q4 | 
	d23 | 1 | 
| 
	q4 | 
	d24 | 1 | 
| 
	q5 | 
	d25 | 1 | 
| 
	q5 | 
	d26 | 1 | 
| 
	q5 | 
	d27 | 1 | 
| 
	q5 | 
	d28 | 1 | 
| 
	q5 | 
	d29 | 1 | 
| 
	q6 | 
	d30 | 1 | 
| 
	q6 | 
	d31 | 1 | 
| 
	q6 | 
	d32 | 1 | 
| 
	q6 | 
	d33 | 1 | 
| 
	q6 | 
	d34 | 1 | 
| 
	q7 | 
	d35 | 1 | 
| 
	q7 | 
	d36 | 1 | 
| 
	q7 | 
	d37 | 1 | 
| 
	q7 | 
	d38 | 1 | 
| 
	q7 | 
	d39 | 1 | 
| 
	q8 | 
	d40 | 1 | 
| 
	q8 | 
	d41 | 1 | 
| 
	q8 | 
	d42 | 1 | 
| 
	q8 | 
	d43 | 1 | 
| 
	q8 | 
	d44 | 1 | 
| 
	q9 | 
	d45 | 1 | 
| 
	q9 | 
	d46 | 1 | 
| 
	q9 | 
	d47 | 1 | 
| 
	q9 | 
	d48 | 1 | 
| 
	q9 | 
	d49 | 1 | 
| 
	q10 | 
	d50 | 1 | 
| 
	q10 | 
	d51 | 1 | 
| 
	q10 | 
	d52 | 1 | 
| 
	q10 | 
	d53 | 1 | 
| 
	q10 | 
	d54 | 1 | 
| 
	q11 | 
	d55 | 1 | 
| 
	q11 | 
	d56 | 1 | 
| 
	q11 | 
	d57 | 1 | 
| 
	q11 | 
	d58 | 1 | 
| 
	q11 | 
	d59 | 1 | 
| 
	q12 | 
	d60 | 1 | 
| 
	q12 | 
	d61 | 1 | 
| 
	q12 | 
	d62 | 1 | 
| 
	q12 | 
	d63 | 1 | 
| 
	q12 | 
	d64 | 1 | 
| 
	q13 | 
	d65 | 1 | 
| 
	q13 | 
	d66 | 1 | 
| 
	q13 | 
	d67 | 1 | 
| 
	q13 | 
	d68 | 1 | 
| 
	q13 | 
	d69 | 1 | 
| 
	q14 | 
	d70 | 1 | 
| 
	q14 | 
	d71 | 1 | 
| 
	q14 | 
	d72 | 1 | 
| 
	q14 | 
	d73 | 1 | 
| 
	q14 | 
	d74 | 1 | 
| 
	q15 | 
	d75 | 1 | 
| 
	q15 | 
	d76 | 1 | 
| 
	q15 | 
	d77 | 1 | 
| 
	q15 | 
	d78 | 1 | 
| 
	q15 | 
	d79 | 1 | 
| 
	q16 | 
	d80 | 1 | 
| 
	q16 | 
	d81 | 1 | 
| 
	q16 | 
	d82 | 1 | 
| 
	q16 | 
	d83 | 1 | 
| 
	q16 | 
	d84 | 1 | 
| 
	q17 | 
	d85 | 1 | 
| 
	q17 | 
	d86 | 1 | 
| 
	q17 | 
	d87 | 1 | 
| 
	q17 | 
	d88 | 1 | 
| 
	q17 | 
	d89 | 1 | 
| 
	q18 | 
	d90 | 1 | 
| 
	q18 | 
	d91 | 1 | 
| 
	q18 | 
	d92 | 1 | 
| 
	q18 | 
	d93 | 1 | 
| 
	q18 | 
	d94 | 1 | 
| 
	q19 | 
	d95 | 1 | 
| 
	q19 | 
	d96 | 1 | 
| 
	q19 | 
	d97 | 1 | 
| 
	q19 | 
	d98 | 1 | 
| 
	q19 | 
	d99 | 1 | 
📚 Translated LONG2RAG (MTEB-Style Retrieval Dataset)
Dataset Summary
This dataset is a translated version of the LONG2RAG benchmark (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).
Supported Tasks and Leaderboards
- Task Category: Retrieval
- Task: Given a natural language query, rank candidate documents by relevance.
- MTEB Integration: Compatible with mtebevaluation 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
@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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