README.md

metadata

license: mit
task_categories:
  - question-answering
language:
  - en
tags:
  - realtime
  - news
configs:
  - config_name: default
    data_files:
      - split: relive
        path:
          - 20250115_qa_public.jsonl
          - 20240705_qa_public.jsonl
          - 20240629_qa_public.jsonl
          - 20240620_qa_public.jsonl
          - 20240613_qa_public.jsonl
          - 20240606_qa_public.jsonl
          - 20240525_qa_public.jsonl
          - 20240519_qa_public.jsonl
          - 20240518_qa_public.jsonl
      - split: realtime
        path:
          - latest/20240119_qa_public.jsonl
          - past/2024/20240112_qa.jsonl
          - past/2024/20240105_qa.jsonl
          - past/2023/20231222_qa.jsonl
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          - past/2022/20221021_qa_public.jsonl
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          - past/2022/20220617-20221014_qa.jsonl
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          - 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:

• https://github.com/realtimeqa/realtimeqa_public (ended Jan 2024?)
• https://github.com/freshllms/freshqa (ongoing, last Dec 2024)

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}
}