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Datasets:
monsoon-nlp
/
relive-qa

Tasks:
Question Answering
Languages:
English
ArXiv:
Tags:
realtime
news
License:
Dataset card Data Studio Files Community
relive-qa / README.md
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metadata
license: mit
task_categories:
  - question-answering
language:
  - en
tags:
  - realtime
  - news
configs:
  - config_name: default
    data_files:
      - split: relive2025_01_15
        path: 20250115_qa_public.jsonl
      - split: relive2024_07_05
        path: 20240705_qa_public.jsonl
      - split: relive2024_06_29
        path: 20240629_qa_public.jsonl
      - split: relive2024_06_20
        path: 20240620_qa_public.jsonl
      - split: relive2024_06_13
        path: 20240613_qa_public.jsonl
      - split: relive2024_06_06
        path: 20240606_qa_public.jsonl
      - split: relive2024_05_25
        path: 20240525_qa_public.jsonl
      - split: relive2024_05_19
        path: 20240519_qa_public.jsonl
      - split: relive2024_05_18
        path: 20240518_qa_public.jsonl
      - split: rtqa2024_01_19
        path: latest/20240119_qa_public.jsonl
      - split: rtqa2024_01_12
        path: past/2024/20240112_qa.jsonl
      - split: rtqa2024_01_05
        path: past/2024/20240105_qa.jsonl
      - split: rtqa2023_12_22
        path: past/2023/20231222_qa.jsonl
      - split: rtqa2023_12_15
        path: past/2023/20231215_qa.jsonl
      - split: rtqa2023_12_08
        path: past/2023/20231208_qa.jsonl
      - split: rtqa2023_12_01
        path: past/2023/20231201_qa.jsonl
      - split: rtqa2023_11_24
        path: past/2023/20231124_qa.jsonl
      - split: rtqa2023_11_17
        path: past/2023/20231117_qa.jsonl
      - split: rtqa2023_11_10
        path: past/2023/20231110_qa.jsonl
      - split: rtqa2023_11_03
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      - split: rtqa2023_10_27
        path: past/2023/20231027_qa.jsonl
      - split: rtqa2023_10_20
        path: past/2023/20231020_qa.jsonl
      - split: rtqa2023_10_13
        path: past/2023/20231013_qa.jsonl
      - split: rtqa2023_10_06
        path: past/2023/20231006_qa.jsonl
      - split: rtqa2023_09_29
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      - split: rtqa2023_09_22
        path: past/2023/20230922_qa.jsonl
      - split: rtqa2023_09_15
        path: past/2023/20230915_qa.jsonl
      - split: rtqa2023_09_08
        path: past/2023/20230908_qa.jsonl
      - split: rtqa2023_09_01
        path: past/2023/20230901_qa.jsonl
      - split: rtqa2023_08_25
        path: past/2023/20230825_qa.jsonl
      - split: rtqa2023_08_18
        path: past/2023/20230818_qa.jsonl
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        path: past/2023/20230811_qa.jsonl
      - split: rtqa2023_08_04
        path: past/2023/20230804_qa.jsonl
      - split: rtqa2023_07_28
        path: past/2023/20230728_qa.jsonl
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        path: past/2023/20230721_qa.jsonl
      - split: rtqa2023_07_14
        path: past/2023/20230714_qa.jsonl
      - split: rtqa2023_07_07
        path: past/2023/20230707_qa.jsonl
      - split: rtqa2023_06_30
        path: past/2023/20230630_qa.jsonl
      - split: rtqa2023_06_23
        path: past/2023/20230623_qa.jsonl
      - split: rtqa2023_06_16
        path: past/2023/20230616_qa.jsonl
      - split: rtqa2023_06_09
        path: past/2023/20230609_qa.jsonl
      - split: rtqa2023_06_02
        path: past/2023/20230602_qa.jsonl
      - split: rtqa2023_05_26
        path: past/2023/20230526_qa.jsonl
      - split: rtqa2023_05_19
        path: past/2023/20230519_qa.jsonl
      - split: rtqa2023_05_12
        path: past/2023/20230512_qa.jsonl
      - split: rtqa2023_05_05
        path: past/2023/20230505_qa.jsonl
      - split: rtqa2023_04_28
        path: past/2023/20230428_qa.jsonl
      - split: rtqa2023_04_21
        path: past/2023/20230421_qa.jsonl
      - split: rtqa2023_04_14
        path: past/2023/20230414_qa.jsonl
      - split: rtqa2023_04_07
        path: past/2023/20230407_qa.jsonl
      - split: rtqa2023_03_31
        path: past/2023/20230331_qa.jsonl
      - split: rtqa2023_03_24
        path: past/2023/20230324_qa.jsonl
      - split: rtqa2023_03_17
        path: past/2023/20230317_qa.jsonl
      - split: rtqa2023_03_10
        path: past/2023/20230310_qa.jsonl
      - split: rtqa2023_03_03
        path: past/2023/20230303_qa.jsonl
      - split: rtqa2023_02_24
        path: past/2023/20230224_qa.jsonl
      - split: rtqa2023_02_17
        path: past/2023/20230217_qa.jsonl
      - split: rtqa2023_02_10
        path: past/2023/20230210_qa.jsonl
      - split: rtqa2023_02_03
        path: past/2023/20230203_qa.jsonl
      - split: rtqa2023_01_27
        path: past/2023/20230127_qa.jsonl
      - split: rtqa2023_01_20
        path: past/2023/20230120_qa.jsonl
      - split: rtqa2023_01_13
        path: past/2023/20230113_qa.jsonl
      - split: rtqa2023_01_06
        path: past/2023/20230106_qa.jsonl
      - split: rtqa2022_12_30
        path: past/2022/20221230_qa.jsonl
      - split: rtqa2022_12_23
        path: past/2022/20221223_qa.jsonl
      - split: rtqa2022_12_16
        path: past/2022/20221216_qa.jsonl
      - split: rtqa2022_12_09
        path: past/2022/20221209_qa.jsonl
      - split: rtqa2022_12_02
        path: past/2022/20221202_qa.jsonl
      - split: rtqa2022_11_25
        path: past/2022/20221125_qa.jsonl
      - split: rtqa2022_11_18
        path: past/2022/20221118_qa.jsonl
      - split: rtqa2022_11_11
        path: past/2022/20221111_qa.jsonl
      - split: rtqa2022_11_03
        path: past/2022/20221104_qa.jsonl
      - split: rtqa2022_10_28
        path: past/2022/20221028_qa.jsonl
      - split: rtqa2022_10_21b
        path: past/2022/20221021_qa.jsonl
      - split: rtqa2022_10_21a
        path: past/2022/20221021_qa_public.jsonl
      - split: rtqa2022_10_14b
        path: past/2022/20221014_qa.jsonl
      - split: rtqa2022_10_14a
        path: past/2022/20220617-20221014_qa.jsonl
      - split: rtqa2022_10_07
        path: past/2022/20221007_qa.jsonl
      - split: rtqa2022_09_30
        path: past/2022/20220930_qa.jsonl
      - split: rtqa2022_09_23
        path: past/2022/20220923_qa.jsonl
      - split: rtqa2022_09_16
        path: past/2022/20220916_qa.jsonl
      - split: rtqa2022_09_09
        path: past/2022/20220909_qa.jsonl
      - split: rtqa2022_09_02
        path: past/2022/20220902_qa.jsonl
      - split: rtqa2022_08_26b
        path: past/2022/20220826_qa.jsonl
      - split: rtqa2022_08_26a
        path: past/2022/20220617-20220826_qa.jsonl
      - split: rtqa2022_08_19
        path: past/2022/20220819_qa.jsonl
      - split: rtqa2022_08_12
        path: past/2022/20220812_qa.jsonl
      - split: rtqa2022_08_05
        path: past/2022/20220805_qa.jsonl
      - split: rtqa2022_07_29
        path: past/2022/20220729_qa.jsonl
      - split: rtqa2022_07_22b
        path: past/2022/20220722_qa.jsonl
      - split: rtqa2022_07_22a
        path: past/2022/20220617-20220722_qa.jsonl
      - split: rtqa2022_07_15b
        path: past/2022/20220715_qa.jsonl
      - split: rtqa2022_07_15a
        path: past/2022/20220617-20220715_qa.jsonl
      - split: rtqa2022_07_08b
        path: past/2022/20220708_qa.jsonl
      - split: rtqa2022_07_08a
        path: past/2022/20220617-20220708_qa.jsonl
      - split: rtqa2022_07_01
        path: past/2022/20220701_qa.jsonl
      - split: rtqa2022_06_24b
        path: past/2022/20220624_qa.jsonl
      - split: rtqa2022_06_24a
        path: past/2022/20220617-20220624_qa.jsonl
      - split: rtqa2022_06_17
        path: past/2022/20220617_qa.jsonl

relive-qa

Using RealtimeQA as a starting point for new articles + Q&A using a semi-automated format.

Also see:

Scraper process

Prerequisites: pip install openai lxml cssselect requests xmltodict and OpenAI API key

I've added these scripts:

  • scrape.py : base script to load plain text from the latest WikiNews articles
  • scrape_with_openai.py : pass scraped text to OpenAI's GPT-4o to generate questions and answers for each article
  • scrape_morerecent_with_openai.py : scrape recent articles which WikiNews hasn't yet published from Category:May 2024, then use OpenAI's GPT-4o for Q&A

An LLM evaluated on this Q&A could read the WikiNews summary, the links collected from the story, and/or do its own web searches.

Issues

  • Prompt should discourage Q&A which are obvious, or stand out from alternative answers.
  • Q&A should be based on new information in the article, and not general knowledge.
  • Links and the article title could give away the answer as the subject of the article, rather than using reading comprehension.
  • WikiNews articles may be niche / local stories, where facts are not known to an LLM unless it reads the specific article

Original paper

Citation:

@inproceedings{
kasai2023realtime,
title={RealTime {QA}: What's the Answer Right Now?},
author={Jungo Kasai and Keisuke Sakaguchi and yoichi takahashi and Ronan Le Bras and Akari Asai and Xinyan Velocity Yu and Dragomir Radev and Noah A. Smith and Yejin Choi and Kentaro Inui},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
eprint={2207.13332},
url={https://openreview.net/forum?id=HfKOIPCvsv}
}