Using Natural Sentence Prompts for Understanding Biases in Language Models

Anonymous

Paper Link: https://openreview.net/forum?id=yqL30wlJi2F

Paper Type: Short paper (up to four pages of content + unlimited references and appendices)

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 sensitive
to 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.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Yi Sun

Copyright Consent Name And Address: MIT