Using Natural Sentence Prompts for Understanding Biases in Language Models
Sarah Alnegheimish, Alicia Guo, Yi Sun
Abstract
Evaluation of biases in language models is often limited to synthetically generated datasets. This dependence traces back to the need of prompt-style dataset to trigger specific behaviors of language models. In this paper, we address this gap by creating a prompt dataset with respect to occupations collected from real-world natural sentences present in Wikipedia.We aim to understand the differences between using template-based prompts and natural sentence prompts when studying gender-occupation biases in language models. We find bias evaluations are very sensitiveto the design choices of template prompts, and we propose using natural sentence prompts as a way of more systematically using real-world sentences to move away from design decisions that may bias the results.
BibTeX
@inproceedings{alnegheimish-etal-2022-using,
title = "Using Natural Sentence Prompts for Understanding Biases in Language Models",
author = "Alnegheimish, Sarah and
Guo, Alicia and
Sun, Yi",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.203/",
doi = "10.18653/v1/2022.naacl-main.203",
pages = "2824--2830"
}