Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairness

TL;DR

This paper advocates for a dual approach to mitigate biases in large language models by combining category-theoretic transformations for structural bias removal and retrieval-augmented generation for contextual bias correction, aiming for fairer AI outputs.

Contribution

It introduces a novel integrated framework that combines functor-based bias mitigation with retrieval-augmented generation to enhance fairness in LLMs.

Findings

Category-theoretic transformations effectively reduce bias while preserving semantics.

Retrieval-augmented generation provides dynamic, context-aware bias correction.

The combined approach offers a robust pathway to fairer language model outputs.

Abstract

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.