CoQA

Not in model cards

Models answer a series of linked conversational questions about a passage, where later questions depend on earlier turns. Scored by word-level F1.

Reported in early LLM papers such as GPT-3.

In the authors’ words

Humans gather information by engaging in conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong conversational and reading comprehension models on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points behind human performance (88.8%), indicating there is ample room for improvement. We launch CoQA as a challenge to the community at http://stanfordnlp.github.io/coqa/

Abstract of CoQA: A Conversational Question Answering Challenge (arXiv:1808.07042, CC0)

Made by Stanford University (Reddy, Chen and Manning) Released Aug 2018 Reading comprehension Public tasks plus a private test set

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.