Estimating Grammatical Gender Directions in Contextual Embeddings under Controlled and Natural Contexts
arXiv:2606.30152v1 Announce Type: cross Abstract: Contextual language models conflate grammatical gender and social semantic bias in gendered languages such as Spanish. Existing gender debiasing approaches only operate on static word embeddings leaving contextual representations unexplored for this two dimensional gender disentanglement. To address the this issue, we make the first attempt to disentangle grammatical gender from semantic contamination for contextual embeddings. We construct both controlled templates and natural Wikipedia contexts to build balanced datasets of inanimate nouns, a
The proliferation of powerful contextual language models necessitates research into mitigating their inherent biases, especially as they become more integrated into critical applications.
Addressing biases in foundational AI models is crucial for their ethical deployment and to prevent the perpetuation of societal harms at scale.
This research provides a novel method for disentangling grammatical gender from semantic bias in contextual embeddings, which could lead to more robust and less biased AI language models.
- · AI ethics researchers
- · Developers of multilingual NLP systems
- · Users of AI in sensitive applications
- · Systems deployed without bias mitigation strategies
Improved performance and fairness of natural language processing applications in gendered languages.
Increased trust in AI systems due to reduced social biases in their outputs.
New regulatory frameworks for AI that mandate bias detection and mitigation at the model architecture level.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI