位置：LLMs必须使用基于函子和RAG驱动的偏见缓解以实现公平性
Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairness
AI总结：
本文提出通过范畴论转换和RAG结合的方法，解决LLMs中的偏见问题，以实现公平性。
摘要：
大语言模型（LLMs）中的偏见往往表现为在人口属性与职业或社会角色之间的系统性扭曲，强化了性别、种族和地理方面的有害刻板印象。本文主张通过双管齐下的方法来解决LLMs中的人口和性别偏见，整合范畴论转换和检索增强生成（RAG）。范畴论提供了一个严谨的、保持结构的数学框架，通过函子将有偏的语义域映射到无偏的规范形式，确保消除偏见的同时保持语义完整性。同时，RAG在推理过程中动态注入多样且最新的外部知识，直接对抗模型参数中的固有偏见。通过结合基于函子的结构性去偏和RAG的上下文基础，我们提出了一个能够提供公平和公正模型输出的全面框架。我们对当前文献的综合验证证明了每种方法的效用，同时解决潜在批评展示了这种整合策略的稳健性。因此，确保LLMs的公平性，需要范畴论转换的数学严谨性和检索增强的适应性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
