ReCoRD
Models fill in a masked entity in a query about a news article, using the article as context. Scoring is exact match and token F1.
ReCoRD is one of the SuperGLUE tasks.
In the authors’ words
We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at http://nlp.jhu.edu/record.
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Short answer A number, word or short string matched to a key.
PaLM 2-L leads with 93.8%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 PaLM 2-L 93.8%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | PaLM 2-L | 93.8% | May 2023 | Model card |
The best score passed 85% in May 2023
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs dropped it from launch posts once the best score passed 85%
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 10 May 2023 |  PaLM 2-L | 93.8 | ReCoRD, 1-shot |
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