---
title: "Igniters@LTEDI 2026 — paper by S et al. (2026)"
description: "The authors' submission to the LT-EDI ACL 2026 shared task tackles two problems linked to gender… 16 references, cited by 1 paper here."
authors: ["Rajendran S", "Ramkumar N", "Malarselvi R"]
year: 2026
venue: "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion"
source: "https://aclanthology.org/2026.ltedi-1.18/"
doi: "10.18653/v1/2026.ltedi-1.18"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/s-2026-igniters-ltedi-2026-multilingual-gender/"
---
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# Igniters@LTEDI 2026: Multilingual Gender-Inclusive Language Generation with mT5 and Counter-Narrative Generation Using Llama-3

**Authors:** Rajendran S, Ramkumar N and Malarselvi R · **Year:** 2026 · **Venue:** [Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion](https://md.research.elamur.ai/free/en/venues/ltedi/) ([Workshop on Language Technology for Equality, Diversity, Inclusion 2026](https://md.research.elamur.ai/free/en/venues/ltedi/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.ltedi-1.18/) · [PDF](https://aclanthology.org/2026.ltedi-1.18.pdf) · [DOI 10.18653/v1/2026.ltedi-1.18](https://doi.org/10.18653/v1/2026.ltedi-1.18) · [OpenAlex](https://openalex.org/W7166860527)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The authors' submission to the LT-EDI ACL 2026 shared task tackles two problems linked to gender bias in text. The first is rewriting gender-marked sentences into neutral form in five languages: English, Tamil, Kannada, German and Spanish. The second is producing counter-narratives that push back on biased English statements. For rewriting, they fine-tuned mT5 with LoRA. They framed the task as minimal-edit sequence-to-sequence transformation and added a Chain-of-Thought-style instruction that asks the model to work out the context before swapping in an inclusive term. For counter-narratives, they gave Llama-3 8B a structured prompt that asks for a short, empathetic and non-insulting reply. An LLM-as-a-Judge framework scored both systems on rubric dimensions. Counter-narrative generation scored very high. Rewriting quality depended heavily on the language: Tamil and Spanish did best, while German and English trailed, mostly because their quality and relevance scores were weak. The study shows that a fairly lightweight setup on one GPU can serve inclusive-language tasks, including in morphologically rich Dravidian languages. However, the paper reports no baselines, ablations or human evaluation.

## Key points
- **Data:** The LT-EDI 2026 shared task data has 7,458 pairs in total. Subtask A contains 2,308 gender-neutral word pairs and 4,424 sentence pairs across five languages; German has only sentence pairs. Subtask B contains 726 English counterfactual pairs.
- **Setup:** Training used Hugging Face Transformers on a single RTX 3090. mT5 was trained with batch size 4, AdamW, a learning rate of 5×10⁻⁵ and 5 epochs. Llama-3 8B decoding used temperature 0.7, top-p 0.9, top-k 50 and a repetition penalty of 1.1.
- **Subtask A averages (mean of GA, GN and QR):** Tamil 92.07, Spanish 81.67, Kannada 74.67, English 60.21 and German 50.51. German's QR score was only 9.09.
- **Subtask B (Llama-3 8B):** PR 95.00, CCNC 95.00 and QR 97.50, for an average of 95.83.
- **Stated limitations:** The authors report semantic drift and over-neutralization in Tamil and Kannada, dependence on the size of the dataset, and subjectivity in LLM-as-a-Judge scoring.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Rajendran S, Ramkumar N and Malarselvi R. 2026. Igniters@LTEDI 2026: Multilingual Gender-Inclusive Language Generation with mT5 and Counter-Narrative Generation Using Llama-3. Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{s2026igniters,
  title = {{Igniters@LTEDI 2026: Multilingual Gender-Inclusive Language Generation with mT5 and Counter-Narrative Generation Using Llama-3}},
  author = {Rajendran S, Ramkumar N and Malarselvi R},
  booktitle = {Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion},
  year = {2026},
  doi = {10.18653/v1/2026.ltedi-1.18},
  url = {https://doi.org/10.18653/v1/2026.ltedi-1.18}
}
```

- [← previous IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation](https://md.research.elamur.ai/free/en/papers/tang-2026-igenbench-benchmarking-reliability-text-infographic/)
- [next → IHLC@LT-EDI 2026: Steering Toward Inclusivity - A Representation Engineering for Gender-Neutral Rewriting](https://md.research.elamur.ai/free/en/papers/p-2026-ihlc-lt-edi-2026-steering/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/13/)

## References

16 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016. [Man is to computer programmer as woman is to homemaker? debiasing word embeddings](https://md.research.elamur.ai/free/en/papers/bolukbasi-2016-man-computer-programmer-woman-homemaker/). In Advances in Neural Information Processing Systems.
2. Bharathi Raja Chakravarthi, Shunmuga Priya Muthusamy Chinnan, Paul Buitelaar, Meghann L. Drury-Grogan, Miguel Ángel García Cumbreras, Salud María Jiménez Zafra, Thomas Mandl, Sylvia Jaki, Rahul Ponnusamy, Anand Kumar M, Dhanalakshmi V, Bharathi B, Premjith B, Senthil Kumar B, and Sathiyaraj T. 2026.… [find](https://scholar.google.com/scholar?q=Bharathi+Raja+Chakravarthi%2C+Shunmuga+Priya+Muthusamy+Chinnan%2C+Paul+Buitelaar%2C+Meghann+L%2E+Drury%2DGrogan%2C+Miguel+%C3%81ngel+Garc%C3%ADa+Cumbreras%2C+Salud+Mar%C3%ADa+Jim%C3%A9nez+Zafra%2C+Thomas+Mandl%2C+Sylvia+Jaki%2C+Rahul+Ponnus)
3. Shunmuga Priya Muthusamy Chinnan, Meghann Drury-Grogan, and Bharathi Raja Chakravarthi. 2025. Gender inclusive language generation framework: A reasoning approach with retrieval augmented generation and chain-of-thought. Knowledge-Based Systems. [find](https://scholar.google.com/scholar?q=Shunmuga+Priya+Muthusamy+Chinnan%2C+Meghann+Drury%2DGrogan%2C+and+Bharathi+Raja+Chakravarthi%2E+2025%2E+Gender+inclusive+language+generation+framework%3A+A+reasoning+approach+with+retrieval+augmented+generation+a)
4. Tony Sun, Andrew Gaut, Shirlyn Tang, and Yuxin Huang. 2019. Mitigating gender bias in natural language processing. In ACL. [find](https://scholar.google.com/scholar?q=Tony+Sun%2C+Andrew+Gaut%2C+Shirlyn+Tang%2C+and+Yuxin+Huang%2E+2019%2E+Mitigating+gender+bias+in+natural+language+processing%2E+In+ACL%2E)
5. Eva Vanmassenhove, Chris Emmery, and Dimitar Shterionov. 2021. Neutral rewriter: A rule-based and neural approach to automatic rewriting into gender-neutral alternatives. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8940–8948. [find](https://scholar.google.com/scholar?q=Eva+Vanmassenhove%2C+Chris+Emmery%2C+and+Dimitar+Shterionov%2E+2021%2E+Neutral+rewriter%3A+A+rule%2Dbased+and+neural+approach+to+automatic+rewriting+into+gender%2Dneutral+alternatives%2E+In+Proceedings+of+the+2021+Co)
6. Tom Watson and 1 others. 2024. Fine-tuning with gender-inclusive language for bias mitigation in large language models. arXiv preprint arXiv:2407.04434. — [arXiv:2407.04434](https://arxiv.org/abs/2407.04434) [find](https://scholar.google.com/scholar?q=Tom+Watson+and+1+others%2E+2024%2E+Fine%2Dtuning+with+gender%2Dinclusive+language+for+bias+mitigation+in+large+language+models%2E+arXiv+preprint+arXiv%3A2407%2E04434%2E+%E2%80%94+%5BarXiv%3A2407%2E04434%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2407)
7. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. [mt5: A massively multilingual pre-trained text-to-text transformer](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/). Proceedings of NAACL-HLT.
8. Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018. [Gender bias in coreference resolution](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/). In NAACL.
9. Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. [Automated hate speech detection and the problem of offensive language](https://md.research.elamur.ai/free/en/papers/davidson-2017-automated-hate-speech-detection-problem/). In ICWSM.
10. Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2019. [Build it break it fix it for dialogue safety: Robustness from adversarial human attack](https://md.research.elamur.ai/free/en/papers/dinan-2019-build-it-break-it-fix/). In EMNLP.
11. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aishwarya Rao, and 1 others. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
12. Antigoni-Maria Founta, Constantinos Djouvas, and Despoina Chatzakou. 2018. Large scale crowdsourcing and characterization of twitter hate speech. In ICWSM. [find](https://scholar.google.com/scholar?q=Antigoni%2DMaria+Founta%2C+Constantinos+Djouvas%2C+and+Despoina+Chatzakou%2E+2018%2E+Large+scale+crowdsourcing+and+characterization+of+twitter+hate+speech%2E+In+ICWSM%2E)
13. Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. 2019. Thou shall not hate: Countering online hate speech. In ICWSM. [find](https://scholar.google.com/scholar?q=Binny+Mathew%2C+Punyajoy+Saha%2C+Seid+Muhie+Yimam%2C+Chris+Biemann%2C+Pawan+Goyal%2C+and+Animesh+Mukherjee%2E+2019%2E+Thou+shall+not+hate%3A+Countering+online+hate+speech%2E+In+ICWSM%2E)
14. Shunmuga Priya Muthusamy Chinnan, Meghann Drury-Grogan, and Bharathi Raja Chakravarthi. 2025. Gender inclusive language generation framework: A reasoning approach with rag and cot. Knowledge-Based Systems, 328:114092. [find](https://scholar.google.com/scholar?q=Shunmuga+Priya+Muthusamy+Chinnan%2C+Meghann+Drury%2DGrogan%2C+and+Bharathi+Raja+Chakravarthi%2E+2025%2E+Gender+inclusive+language+generation+framework%3A+A+reasoning+approach+with+rag+and+cot%2E+Knowledge%2DBased+Sys)
15. Debora Nozza, Federico Bianchi, and Dirk Hovy. 2019. What the mask? making sense of gender bias in bert. In ACL Workshop on Gender Bias in NLP. [find](https://scholar.google.com/scholar?q=Debora+Nozza%2C+Federico+Bianchi%2C+and+Dirk+Hovy%2E+2019%2E+What+the+mask%3F+making+sense+of+gender+bias+in+bert%2E+In+ACL+Workshop+on+Gender+Bias+in+NLP%2E)
16. Andrea Piergentili, Beatrice Savoldi, Matteo Negri, and Luisa Bentivogli. 2025. Gender-neutral rewriting in italian: Models, approaches, and trade-offs. arXiv preprint arXiv:2509.13480. [find](https://scholar.google.com/scholar?q=Andrea+Piergentili%2C+Beatrice+Savoldi%2C+Matteo+Negri%2C+and+Luisa+Bentivogli%2E+2025%2E+Gender%2Dneutral+rewriting+in+italian%3A+Models%2C+approaches%2C+and+trade%2Doffs%2E+arXiv+preprint+arXiv%3A2509%2E13480%2E)

*Updated 31 October 2026*