MISGENDERED: Limits of Large Language Models in Understanding Pronouns
Abstract
Gender bias in language technologies has been widely studied, but research has mostly been restricted to a binary paradigm of gender.
It is important to also consider non-binary gender identities, as excluding them can cause further harm to an already marginalized group.
One way in which English-speaking individuals linguistically encode their gender identity is through third-person personal pronoun declarations.
This is often done using two or more pronoun forms, e.g., \textit{xe/xem}, or \textit{xe/xem/xyr}.
In this paper, we comprehensively evaluate state-of-the-art language models for their ability to correctly use declared third-person personal pronouns.
As far as we are aware, we are the first to do so.
We evaluate language models in both zero-shot and few-shot settings.
Models are still far from zero-shot gendering non-binary individuals accurately, and most also struggle with correctly using gender-neutral pronouns (singular \textit{they, them, their} etc.).
This poor performance may be due to the lack of representation of non-binary pronouns in pre-training corpora, and some memorized associations between pronouns and names.
We find an overall improvement in performance for non-binary pronouns when using in-context learning, demonstrating that language models with few-shot capabilities can adapt to using declared pronouns correctly.
It is important to also consider non-binary gender identities, as excluding them can cause further harm to an already marginalized group.
One way in which English-speaking individuals linguistically encode their gender identity is through third-person personal pronoun declarations.
This is often done using two or more pronoun forms, e.g., \textit{xe/xem}, or \textit{xe/xem/xyr}.
In this paper, we comprehensively evaluate state-of-the-art language models for their ability to correctly use declared third-person personal pronouns.
As far as we are aware, we are the first to do so.
We evaluate language models in both zero-shot and few-shot settings.
Models are still far from zero-shot gendering non-binary individuals accurately, and most also struggle with correctly using gender-neutral pronouns (singular \textit{they, them, their} etc.).
This poor performance may be due to the lack of representation of non-binary pronouns in pre-training corpora, and some memorized associations between pronouns and names.
We find an overall improvement in performance for non-binary pronouns when using in-context learning, demonstrating that language models with few-shot capabilities can adapt to using declared pronouns correctly.
Research Areas
