In-Depth Look at Word Filling Societal Bias Measures

Autor:	Pikuliak, Matúš, Beňová, Ivana, Bachratý, Viktor
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Many measures of societal bias in language models have been proposed in recent years. A popular approach is to use a set of word filling prompts to evaluate the behavior of the language models. In this work, we analyze the validity of two such measures -- StereoSet and CrowS-Pairs. We show that these measures produce unexpected and illogical results when appropriate control group samples are constructed. Based on this, we believe that they are problematic and using them in the future should be reconsidered. We propose a way forward with an improved testing protocol. Finally, we also introduce a new gender bias dataset for Slovak. Comment: EACL 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.12640 Zobrazit plný text záznamu View this record from Arxiv