Counterfactual based gender fairness algorithm for large language models

Abstract

Gender bias assessment techniques that are available currently only consider two genders viz male and female and is not inclusive of lesbian, gay, bisexual, transgender and queer (LGBTQ) genders. Present disclosure provides method and system for assessing gender fairness of large language models (LLM). The system first receive context aware prompt comprising context and prompt and then converts names present in the context using name anonymization technique. The system then convert many-to-many pronouns present in no-name-context into many to one pronouns which are then used to create counterfactual context for each gender pronoun group. Thereafter, system queries LLM with counterfactual prompt created using counterfactual context to obtain counterfactual response which is then used to create embedding data frame. Further, system trains Gaussian Mixture Model using embedding data frame which is then utilized to calculate cluster distances. Finally, system assess gender bias based on cluster distances and predefined distance threshold.