@inproceedings{ramrakhiyani-etal-2023-zero,
title = "Zero-shot Probing of Pretrained Language Models for Geography Knowledge",
author = "Ramrakhiyani, Nitin and
Varma, Vasudeva and
Palshikar, Girish and
Pawar, Sachin",
editor = {Deutsch, Daniel and
Dror, Rotem and
Eger, Steffen and
Gao, Yang and
Leiter, Christoph and
Opitz, Juri and
R{\"u}ckl{\'e}, Andreas},
booktitle = "Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems",
month = nov,
year = "2023",
address = "Bali, Indonesia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eval4nlp-1.5",
doi = "10.18653/v1/2023.eval4nlp-1.5",
pages = "49--61",
abstract = "Gauging the knowledge of Pretrained Language Models (PLMs) about facts in niche domains is an important step towards making them better in those domains. In this paper, we aim at evaluating multiple PLMs for their knowledge about world Geography. We contribute (i) a sufficiently sized dataset of masked Geography sentences to probe PLMs on masked token prediction and generation tasks, (ii) benchmark the performance of multiple PLMs on the dataset. We also provide a detailed analysis of the performance of the PLMs on different Geography facts.",
}
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%0 Conference Proceedings
%T Zero-shot Probing of Pretrained Language Models for Geography Knowledge
%A Ramrakhiyani, Nitin
%A Varma, Vasudeva
%A Palshikar, Girish
%A Pawar, Sachin
%Y Deutsch, Daniel
%Y Dror, Rotem
%Y Eger, Steffen
%Y Gao, Yang
%Y Leiter, Christoph
%Y Opitz, Juri
%Y Rücklé, Andreas
%S Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems
%D 2023
%8 November
%I Association for Computational Linguistics
%C Bali, Indonesia
%F ramrakhiyani-etal-2023-zero
%X Gauging the knowledge of Pretrained Language Models (PLMs) about facts in niche domains is an important step towards making them better in those domains. In this paper, we aim at evaluating multiple PLMs for their knowledge about world Geography. We contribute (i) a sufficiently sized dataset of masked Geography sentences to probe PLMs on masked token prediction and generation tasks, (ii) benchmark the performance of multiple PLMs on the dataset. We also provide a detailed analysis of the performance of the PLMs on different Geography facts.
%R 10.18653/v1/2023.eval4nlp-1.5
%U https://aclanthology.org/2023.eval4nlp-1.5
%U https://doi.org/10.18653/v1/2023.eval4nlp-1.5
%P 49-61
Markdown (Informal)
[Zero-shot Probing of Pretrained Language Models for Geography Knowledge](https://aclanthology.org/2023.eval4nlp-1.5) (Ramrakhiyani et al., Eval4NLP-WS 2023)
ACL