A PCA gender direction carries first-name discriminative signal

An et al. approximate a female-male gender direction via PCA over gendered word pairs, evaluated on 470 first names' contextualized embeddings [an-etal-2025-on-the-mutual-influence-of-gender-and-occupation-in-llm-representations] The models are Llama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.3, OLMo-7B-0724-hf, and Phi-3.5-mini-instruct [an-etal-2025-on-the-mutual-influence-of-gender-and-occupation-in-llm-representations] The 1-D gender-direction projection achieves 75.18% binary gender-classification accuracy versus 75.46% for the full embedding (Llama-3.1-8B) [an-etal-2025-on-the-mutual-influence-of-gender-and-occupation-in-llm-representations] First-name gender representations shift with occupational context, e.g. "nurse" pulls embeddings toward femininity [an-etal-2025-on-the-mutual-influence-of-gender-and-occupation-in-llm-representations] The internal gender-direction coefficient correlates with downstream occupation-prediction bias (Spearman rho=0.61 Llama-3.1-8B, 0.76 Mistral-7B; both p<0.001) [an-etal-2025-on-the-mutual-influence-of-gender-and-occupation-in-llm-representations]