Linear socio-demographic representations emerge in Large Language Models from indirect cues

This article reports on research exploring how Large Language Models (LLMs) develop representations of socio-demographic information. The key finding is that these representations, such as those related to gender or ethnicity, emerge linearly within the model, even when not explicitly trained on such data. This suggests that LLMs learn these associations indirectly from the statistical patterns present in the training data. The research likely investigates the implications of this for bias and fairness in LLMs.

• •LLMs develop linear representations of socio-demographic information.
• •These representations emerge from indirect cues in the training data.
• •The research likely explores implications for bias and fairness.