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We investigate the role of large language mod els (LLMs) in promoting gender-inclusive lan guage by evaluating their ability to rewrite bi ased text and generate counterfactual narratives across multiple languages. We introduce a shared task with two subtasks: gender-inclusive rewriting and counterfactual generation. The task covers five languages English, German, Spanish, Tamil, and Kannada reflecting diverse grammatical gender systems and sociocultural contexts. We release curated word-level and sentence-level datasets to support controlled inclusive generation. A total of 50 teams reg istered for the shared task, and around 8 teams submitted results. Submissions are evaluated using a hybrid framework combining rubric based automatic scoring with expert human judgment. Finally, we provide an overview of participating systems and discuss key findings and challenges observed across languages.

ACL 2026

Insights from Multilingual Gender Inclusive Language Generation Shared Task

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Gender bias in multilingual language gener ation systems poses serious ethical and so cial issues, especially in languages with com plex morphology. In this study, we propose a lightweight multilingual approach that employs instruction-guided fine-tuning of the mT5 small transformer model for gender-inclusive language generation. The framework accom modates five languages: English, German, Spanish, Tamil, and Kannada. The approach uses the task-prefix rewriting method to trans form gender-specific sentences to their gender neutral versions. The training data from differ ent languages is combined into a single multi lingual dataset for sequence-to-sequence fine tuning. Beam search decoding with repetition constraints is used during inference to improve the quality of the output. The system’s perfor mance is measured using GIFI, semantic simi larity, 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.

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

The rapid growth of social media has also led to a rise in abusive and harmful content, which negatively affects the online environment for users. The frequent use of offensive language and hate speech contributes to making these platforms increasingly hostile. In particular, homophobic and transphobic remarks target members of the LGBT+ community. Detecting such comments is therefore essential so that they can be flagged promptly and appropriate warnings can be given to users involved in such behaviour. The problem becomes more serious when such content appears in other forms of communication used by younger generations, such as memes. This work tries to address this issue. We propose a method to detect such content using the meme dataset from the LT-EDI 2026 challenge and secured 8th rank for English and 6th rank for Chinese language dataset in the shared task. Our approach uses a multimodal technique that processes both image and text information. The dataset has limited data, which creates a challenge. To handle this, we pre–fine-tune the models on a similar dataset called PrideMM. The proposed multimodal approach achieved Macro F1-scores of 0.24 and 0.57 for English and Chinese memes respectively

CuriousVectors@LT-EDI 2026: Detection of Homophobic and Transphobic Memes on Social Media Using a Hybrid Multimodal Approach

Online hate speech is spreading rapidly, creat ing significant challenge, particularly in low resource language such as Tamil. Lack of de veloped automated content moderation systems makes it difficult to control harmful content effectively. In this study, we propose a com putational framework for generating Counter Narratives (CNs) using classical NLP tech niques. With this, we leverage TF-IDF fea tures with n-grams to identify the labels as Ho mophobic or Transphobic. Span detection is performed with TF-IDF features with n-grams and Machine learning models. Counter narra tives are then retrieved by computing cosine similarity, ensuring semantic alignment and contextual relevance. Evaluation on the ex panded human curated dataset demonstrates that our approach produces contextually appro priate and semantically coherent counter nar ratives. Notably, the proposed system is sub mitted at Task 2 shown a overall average score of 80.40 % for Tamil and 77.29 % for English and secured first and fourth rank respectively. GitHub: https://github.com/kannanrrk/Span Counter-Feature-Based

DLRG@LT-EDI 2026: Automating Counter-Narratives for Homophobic and Transphobic Comments

The detection and response to homophobic and transphobic comments are important chal lenges in Natural Language Processing. In this paper, we focus on the detection of span for homophobic and transphobic comments (Task 1) and generation of counter narratives for abu sive comments (Task 2) for the LT-EDI @ ACL 2026 shared task. Harmful comments made online against the LGBTQ+ community have created a hostile environment for users. In this paper, we have used the transformer model for the detection of span for homophobic and trans phobic comments and generation of counter narratives. In this task, the detection of the span of comments containing homophobic and trans phobic words and the generation of counter nar ratives for abusive comments have been done using the transformer model. The results show the efficiency of the transformer model in the detection of the span of comments and gener ation of counter narratives. This paper empha sizes the efficiency of the transformer model in creating a safe environment for users.

DuoNova@LTEDI 2026: Multilingual Span Detection and Counter-Narrative Generation on Homophobic and Transphobic Comments

The deployment of Large Language Models (LLMs) has intensified concerns regarding the propagation of societal stereotypes encoded with web-scale training corpora. This pa per presents a dual-paradigm framework spe cially designed to address multilingual gender inclusivity and counterfactual generation. For multilingual gender-neutral text transformation, a fine-tuned mT5 encoder–decoder model per forms controlled sentence rewriting with mini mal edits while preserving semantic fidelity and grammatical fluency. For counter-narrative gen eration, the Llama-3 8B decoder-only model is employed to generate empathetic and persua sive responses through structured prompt-based generation. The framework is evaluated using datasets from the LT-EDI ACL 2026 shared task across multiple languages, including En glish, Tamil, Kannada, German, and Spanish. Experimental results demonstrate strong effec tiveness in identifying and neutralizing gender markers, particularly in morphologically rich languages, while the counter-narrative compo nent achieves high performance in politeness, coherence, and relevance. Overall, the pro posed approach contributes toward the develop ment of responsible and inclusive multilingual NLPsystems.

Igniters@LTEDI 2026: Multilingual Gender-Inclusive Language Generation with mT5 and Counter-Narrative Generation Using Llama-3

This paper describes the IHLC team’s submis sion to the LT-EDI 2026 shared task on gender inclusive language generation. For Subtask A(gender-neutral rewriting), we applied Low Rank Adaptation (LoRA) fine-tuning, achiev ing an 80.00% evaluation score and placing 3rd. Our primary methodological focus, how ever, is Subtask B (counter-narrative genera tion), where we propose a compute-efficient representation engineering approach. We com pute a PCA-derived steering direction from counterfactual activations and inject it into the Gemma-3-4B-it model at inference time, shift ing behavior toward inclusivity without weight updates. Paired with constrained prompting, this yielded polite, context-aware responses and a 78.12% score (Rank 6). We conclude with a manual evaluation of steering failure modes, detailing critical trade-offs in semantic preser vation and over-steering instability.

IHLC@LT-EDI 2026: Steering Toward Inclusivity - A Representation Engineering for Gender-Neutral Rewriting

This paper presents our submissions to the LT EDI@ACL 2026 Shared Task on Gender Inclu sive Language Generation. The task focuses on controlled text rewriting that reduces gen der bias while keeping the original meaning and fluency intact. We participated in both the subtasks and treated them independently, training separate instances of the instruction tuned encoder–decoder model on the respective training datasets. Scores are calculated based on averages across different rubrics, including Gender Assumption (GA), Gender Neutrality (GN), and Quality Relevance (QR) for Task A, and Politeness and Respectful (PR), Con textual Counter-Narrative Coherence (CCNC), and Quality and Relevance (QR) for Task B. For Subtask A (Gender-Inclusive Language Generation) in the English dataset, an aver age score of 43.7917 could be achieved. For Subtask B (Counterfactual Generation), we achieved an average score of 82.6241. Over all, the experiments indicate that full finetuning of instruction-tuned transformers provides an effective way to produce sentence in gender neutral form and also producing counter-factual sentences for biased one, when each subtask is optimized on its own data.

IReL_IIT(BHU)@LTEDI 2026: Fine-Tuning Instruction-Tuned Transformers for Gender-Inclusive Rewriting and Counterfactual Bias Mitigation

Online platforms increasingly host hate speech targeting marginalized communities, includ ing homophobic and transphobic comments directed at LGBTQ+ individuals. Counter narratives provide a constructive way to re spond to harmful speech by promoting em pathy, factual clarification, and respectful di alogue. In this work, we participate in the Shared Task on Counter-Narrative Generation on Homopho bic and Transphobic Comments at LT-EDI @ ACL 2026. We adopt a zero-shot prompting approach using large language models accessed through publicly available AI tools, including GPT-4o, Gemini 1.5 Pro, and Llama-3 Sonar Large via Perplexity AI. Instead of training a task-specific model, we design a structured prompt that guides the models to generate re spectful, concise, and contextually appropriate counter-narratives. Experiments were conducted on English and Tamil comments provided by the organiz ers. Results demonstrate that prompt-based generation can produce meaningful multilin gual counter-narratives without additional train ing. Our approach highlights the potential of large language models as lightweight tools for counter-speech generation in multilingual on line environments.

JusticeBots@LT-EDI 2026: Prompt-Based Counter-Narrative Generation for Homophobia and Transphobia Comments

Over the past decade, the rapid advancement of LLMs has significantly improved natural language generation. However, these mod els often inherit and amplify gender biases present in large-scale training data, leading to stereotypical associations, androcentric lan guage, and misgendering. Such biases can negatively impact applications in education, healthcare, legal systems, and automated con tent generation. In this paper, we address this issue as defined in the shared task LT-EDI on Gender-Inclusive Language Generation. The task focuses on rewriting gender-biased sen tences into inclusive, gender-neutral alterna tives while preserving meaning. We propose a retrieval-augmented framework combining lexical replacement, semantic retrieval, and controlled instruction-tuned generation. An edit-distance constraint and self-evaluation step ensure minimal, coherent, and bias-free out puts. We also present zero-shot adaptation for low-resource languagea. The implementation code is available here https://github.com/ SupriyaChanda/gilg-ltedi-acl2026.git.

JustGen@LT-EDI 2026: Controlled Gender Inclusive and Bias-Aware Language Generation using LLMs

Counter Narrative (CN) generation via Large Language Models (LLMs) offers a scalable ap proach to combating hate speech by produc ing targeted responses that challenge harmful content. However, existing methods typically require costly post-training or fine-tuning to improve narrative diversity and quality. We introduce a fine-tuning-free prompt optimiza tion technique that enhances Counter Narrative effectiveness without additional model train ing, making it both resource-efficient and read ily deployable. We conduct extensive evalua tion on hate speech datasets spanning English and Tamil, employing both reference-based metrics and rubric-based LLM-as-a-judge scor ing to capture multiple dimensions of nar rative quality. Experiments across multiple LLMs demonstrate that our approach consis tently outperforms vanilla prompting baselines, exhibits strong transferability across models, and adapts seamlessly to new evaluation met rics—requiring no architectural or procedu ral changes. Our findings suggest that care fully optimized prompting strategies can match or exceed the performance of more resource intensive approaches, offering a practical path toward scalable hate speech intervention.

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