README.md

metadata

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 (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).

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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.

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Languages

• Original: English
• This release: Translated into Persian

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Dataset Details

Queries

• 280 complex, uncontaminated, long-form questions.

Corpus

• Retrieved real-world documents (5 per query).

Relevance Labels

• Binary (relevant / not relevant).

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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

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Data Splits

• test: 280 queries

Each query has 5 candidate documents, aligned with MTEB retrieval style.

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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}
}