Gender inclusive language generation framework: A reasoning approach with RAG and CoT

Muthusamy Chinnan, Shunmuga Priya, Drury-Grogan, Meghann, & Chakravarthi, Bharathi Raja. (2025). Gender inclusive language generation framework: A reasoning approach with RAG and CoT. Knowledge-Based Systems, 328, 114092. https://doi.org/10.1016/j.knosys.2025.114092

Abstract

Language is a dynamic and evolving concept that shapes thought and perception. The increasing reliance on Natural Language Processing models necessitates careful consideration of their alignment with inclusive language practices. However, Large Language Models often perpetuate biases due to training on androcentric and stereotypical data, undermining fairness and inclusivity. To address this, we propose a novel Two-Pass Retrieval Augmented Generation RAG with Chain of Thought framework that first retrieves contextual, unbiased references from a created corpus of inclusive texts and then applies structured, step-by-step reasoning via CoT prompting to enhance inclusivity in LLM output. By systematically retrieving relevant, unbiased references and enforcing structured reasoning, the framework promotes the generation of more inclusive and less biased content. Both LLM and human based evaluation using structured prompts with metrics like gender assumption, gender neutrality and quality and relevance Score are utilized. The text completion and generation tasks demonstrate that the proposed framework reduces gender bias.

Funder

Research Ireland

Publisher

Elsevier

Rights

CC BY
CC BY

cb

Collections

Computer Science (Scholarly Articles)