Fembeddings

Fembeddings is a term used in discussions of natural language processing and representation learning to describe dense vector representations that encode gender-related information found in text data. The term is informal and not widely standardized; it may refer to embeddings trained on corpora with gendered language, or to embedding spaces that researchers analyze to study or mitigate gender bias, sometimes described as feminine and masculine directions in the latent space.

Origin and usage: The word appears in blogs, workshop proceedings, and some research papers as a colloquial label rather than an established technical category. It is often discussed in the context of bias, fairness, and representation in NLP.

Technical aspects: In practice, neural embeddings map tokens to vectors; fembeddings would be those portions of the embedding space that correlate with gendered associations. Methods include measuring gender bias with prompts or templates, probing studies to detect gender signals, and applying debiasing approaches to reduce gender information, or preserving signals for interpretability.

Applications and implications: Fembeddings are relevant to bias auditing, model fairness, and content generation. Critics warn that encoding or emphasizing gender signals risks reinforcing stereotypes or treating gender as a fixed binary attribute; ethical considerations are central.

See also: word embedding, bias in AI, debiasing, gender bias.