Work

CAI@LTEDI 2026: Multilingual Gender Inclusive Language Generation using Instruction-Guided mT5 Transformer Model

Year: 2026

Type: conference-paper

Abstract: Gender bias in multilingual language generation systems poses serious ethical and social issues, especially in languages with complex morphology.In this study, we propose a lightweight multilingual approach that employs instruction-guided fine-tuning of the mT5small transformer model for gender-inclusive language generation.The framework accommodates five languages: English, German, Spanish, Tamil, and Kannada.The approach uses the task-prefix rewriting method to transform gender-specific sentences to their genderneutral versions.The training data from different languages is combined into a single multilingual dataset for sequence-to-sequence finetuning.Beam search decoding with repetition constraints is used during inference to improve the quality of the output.The system's performance is measured using GIFI, semantic similarity, and an overall combined score across all languages.Experimental results show that the system can eliminate gender-biased language while retaining semantic meaning in part across languages.

Source: Workshop on Language Technology for Equality, Diversity, Inclusion (LT-EDI)

DOI: 10.18653/v1/2026.ltedi-1.14

Authors: Aiswariya p Nair, Sree S Bhagya, Chinnu Jacob

Institution: TKM College of Engineering

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Reduced inequalities, Gender equality

Open Access status: hybrid

OpenAlex ID: W7166826319