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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, medium

Summary

A lightweight multilingual approach for gender-inclusive language generation utilizes instruction-guided fine-tuning of the mT5-small transformer model. This framework supports five languages: English, German, Spanish, Tamil, and Kannada. It employs a task-prefix rewriting method to transform gender-specific sentences into gender-neutral versions. Training data from these diverse languages is consolidated into a single multilingual dataset for sequence-to-sequence fine-tuning. During inference, beam search decoding with repetition constraints enhances output quality. The system's performance is assessed using GIFI, semantic similarity, and an overall combined score across all languages. Experimental results demonstrate the system's ability to eliminate gender-biased language while partially preserving semantic meaning across the supported languages.

Key takeaway

For NLP Engineers developing multilingual language generation systems, you should consider implementing instruction-guided fine-tuning on models like mT5-small. This approach effectively mitigates gender bias across languages such as English, German, Spanish, Tamil, and Kannada, while largely maintaining semantic integrity. You can enhance output quality by incorporating beam search decoding with repetition constraints during inference. This method offers a lightweight solution to improve fairness in your language models.

Key insights

Instruction-guided fine-tuning of mT5-small enables multilingual gender-inclusive language generation across five languages.

Principles

• Gender bias poses serious ethical and social issues.
• Multilingual data improves model generalization.
• Task-prefix rewriting aids gender neutrality.

Method

Fine-tune mT5-small with instruction-guided task-prefix rewriting on a combined multilingual dataset for sequence-to-sequence gender-neutral transformation. Use beam search decoding.

In practice

• Apply task-prefix rewriting for neutrality.
• Combine diverse language datasets.
• Use beam search for output quality.

Topics

• Multilingual NLP
• Gender Bias Mitigation
• mT5 Transformer
• Instruction Tuning
• Language Generation
• Sequence-to-Sequence Models

Best for: Research Scientist, AI Scientist, NLP Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.