[2603.21524] CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMs

Computer Science > Computation and Language arXiv:2603.21524 (cs) [Submitted on 23 Mar 2026] Title:CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMs Authors:Ravi Ranjan, Utkarsh Grover, Mayur Akewar, Xiaomin Lin, Agoritsa Polyzou View a PDF of the paper titled CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMs, by Ravi Ranjan and Utkarsh Grover and Mayur Akewar and Xiaomin Lin and Agoritsa Polyzou View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are deployed in high-stakes settings but can show demographic, gender, and geographic biases that undermine fairness and trust. Prior debiasing methods, including embedding-space projections, prompt-based steering, and causal interventions, often act at a single stage of the pipeline, resulting in incomplete mitigation and brittle utility trade-offs under distribution shifts. We propose CatRAG Debiasing, a dual-pronged framework that integrates functor with Retrieval-Augmented Generation (RAG) guided structural debiasing. The functor component leverages category-theoretic structure to induce a principled, structure-preserving projection that suppresses bias-associated directions in the embedding space while retaining task-relevant semantics. On the Bias Benchmark for Question Answering (BBQ) across three open-source LLMs (Meta Llama-3, OpenAI GPT-OSS, and Google Gemma-3), CatRAG achieves state-of-the-art results, improving accuracy by up to 40%...