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
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
@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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