@inproceedings{summers-stay-etal-2021-generative,
title = "What Can a Generative Language Model Answer About a Passage?",
author = "Summers-Stay, Douglas and
Bonial, Claire and
Voss, Clare",
editor = "Fisch, Adam and
Talmor, Alon and
Chen, Danqi and
Choi, Eunsol and
Seo, Minjoon and
Lewis, Patrick and
Jia, Robin and
Min, Sewon",
booktitle = "Proceedings of the 3rd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrqa-1.7",
doi = "10.18653/v1/2021.mrqa-1.7",
pages = "73--81",
abstract = "Generative language models trained on large, diverse corpora can answer questions about a passage by generating the most likely continuation of the passage followed by a question/answer pair. However, accuracy rates vary depending on the type of question asked. In this paper we keep the passage fixed, and test with a wide variety of question types, exploring the strengths and weaknesses of the GPT-3 language model. We provide the passage and test questions as a challenge set for other language models.",
}
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<abstract>Generative language models trained on large, diverse corpora can answer questions about a passage by generating the most likely continuation of the passage followed by a question/answer pair. However, accuracy rates vary depending on the type of question asked. In this paper we keep the passage fixed, and test with a wide variety of question types, exploring the strengths and weaknesses of the GPT-3 language model. We provide the passage and test questions as a challenge set for other language models.</abstract>
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%0 Conference Proceedings
%T What Can a Generative Language Model Answer About a Passage?
%A Summers-Stay, Douglas
%A Bonial, Claire
%A Voss, Clare
%Y Fisch, Adam
%Y Talmor, Alon
%Y Chen, Danqi
%Y Choi, Eunsol
%Y Seo, Minjoon
%Y Lewis, Patrick
%Y Jia, Robin
%Y Min, Sewon
%S Proceedings of the 3rd Workshop on Machine Reading for Question Answering
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F summers-stay-etal-2021-generative
%X Generative language models trained on large, diverse corpora can answer questions about a passage by generating the most likely continuation of the passage followed by a question/answer pair. However, accuracy rates vary depending on the type of question asked. In this paper we keep the passage fixed, and test with a wide variety of question types, exploring the strengths and weaknesses of the GPT-3 language model. We provide the passage and test questions as a challenge set for other language models.
%R 10.18653/v1/2021.mrqa-1.7
%U https://aclanthology.org/2021.mrqa-1.7
%U https://doi.org/10.18653/v1/2021.mrqa-1.7
%P 73-81
Markdown (Informal)
[What Can a Generative Language Model Answer About a Passage?](https://aclanthology.org/2021.mrqa-1.7) (Summers-Stay et al., MRQA 2021)
ACL