Biases in large language models (LLMs) often manifest as systematic distortions in associations between demographic attributes and professional or social roles, reinforcing harmful stereotypes across gender, ethnicity, and geography. This position paper advocates for addressing demographic and gender biases in LLMs through a dual-pronged methodology, integrating category-theoretic transformations and retrieval-augmented generation (RAG). Category theory provides a rigorous, structure-preserving mathematical framework that maps biased semantic domains to unbiased canonical forms via functors, ensuring bias elimination while preserving semantic integrity. Complementing this, RAG dynamically injects diverse, up-to-date external knowledge during inference, directly countering ingrained biases within model parameters. By combining structural debiasing through functor-based mappings and contextual grounding via RAG, we outline a comprehensive framework capable of delivering equitable and fair model outputs. Our synthesis of the current literature validates the efficacy of each approach individually, while addressing potential critiques demonstrates the robustness of this integrated strategy. Ensuring fairness in LLMs, therefore, demands both the mathematical rigor of category-theoretic transformations and the adaptability of retrieval augmentation.

翻译：大型语言模型（LLMs）中的偏置通常表现为人口统计属性与职业或社会角色之间系统性的关联扭曲，强化了跨性别、种族和地域的有害刻板印象。本立场论文主张通过双管齐下的方法论解决LLMs中的人口统计与性别偏置，即整合范畴论变换与检索增强生成（RAG）。范畴论提供了一个严谨的、保持结构的数学框架，通过函子将偏置语义域映射至无偏规范形式，在保持语义完整性的同时确保偏置消除。与此互补，RAG在推理过程中动态注入多样化、最新的外部知识，直接对抗模型参数中根深蒂固的偏置。通过结合基于函子映射的结构性去偏与RAG的语境锚定，我们提出了一个能够实现公平模型输出的综合框架。我们对现有文献的综合分析验证了每种方法单独的有效性，而对潜在批评的讨论则证明了这种集成策略的鲁棒性。因此，确保LLMs的公平性既需要范畴论变换的数学严谨性，也离不开检索增强的适应性。