Question Answering in Context
Question Answering in Context is a large-scale dataset that consists of around 14K crowdsourced Question Answering dialogs with 98K question-answer pairs in total. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text.
Source: https://paperswithcode.com/paper/quac-question-answering-in-context-1/
Image Source: https://paperswithcode.com/paper/quac-question-answering-in-context-1/
Variants: QuAC
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Question Answering | GPT-3 175B (few-shot, k=32) | Language Models are Few-Shot Learners | 2020-05-28 |
| Question Answering | FlowQA (single model) | FlowQA: Grasping Flow in History … | 2018-10-06 |
Recent papers with results on this dataset: