United States

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.

ACL 2026

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

flan-t5 and mt5

counter-narrative generation

multilingual natural language processing (nlp)

homophobia and transphobia detection

span detection

transformer models

hate speech detection

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

workshop paper

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.

NEUNI@LT-EDI 2026: Counter Narrative Generation on Homophobic and Transphobic Comments

The problem of harmful online discourse against the LGBTQ+ community is still a con cern on social media platforms. Although hate speech detection is a well-explored area, the task of constructive counter-narrative genera tion is still an emerging field of research, es pecially in the multilingual and low-resource settings. Counter-narratives are designed to counter harmful discourse with respectful and empathetic responses, as opposed to mere content deletion. In this paper, the model proposes a zero-shot multilingual system for counter-narrative generation in English and Tamil. The proposed system employs the pretrained google/flan-t5-base transformer model guided by rubric-aligned prompts to en courage politeness, contextual relevance, and non-toxic response generation. The system operates in a zero-shot setting without task specific fine-tuning and uses beam search de coding for controlled response generation. On the English test data, the system scored an over all score of 70.33 per cent with a contextual coherence score of 81.82 per cent. On the Tamil test data, the system scored an overall score of 33.57 per cent with significantly lower scores on coherence and quality. These find ings indicate that structured prompting can fa cilitate safe and coherent generation in English, but also underscore the challenges of zero-shot multilingual models in low-resource language scenarios.

RspectNLP@LT-EDI 2026: Rubric-Driven Prompting for Safe Multilingual Counter Narrative Generation

Social media is now an important platform for communication and interaction. At the same time, the amount of abusive and harmful con tent online has also increased. Offensive lan guage and hate speech are making these plat forms less safe and less welcoming for users. Many of these contents include homophobic and transphobic remarks aimed at the LGBT+ community. Such behaviour damages healthy discussions and can negatively affect users. For this reason, it is important to detect these con tents early so they can be flagged and removed to maintain a healthy online well-being. The is sue becomes more difficult when harmful mes sages appear in popular formats like memes. Memes are widely used by younger users to communicate online. Because they combine images and text, detecting offensive meaning becomes challenging. In this work, we attempt to address this problem. We develop a method to identify such content using the meme dataset released for the LT-EDI 2026 challenge and se cured rank 5 in the shared task. We propose a Zero-shot learning based method employ ing two LLMs (Qwen2.5-VL-3B-Instruct and Meta-Llama-3-8B-Instruct) to generate descrip tions and classify such memes. We achieved a macro F1-score of 0.55 for the English lan guage meme.

SAJI_English@LT-EDI 2026: Detection of Homophobia and Transphobia in Internet Memes Using Zero-Shot Learning

Automated detection of LGBTQ+ phobia is critical for digital safety. We participated in the LT-EDI@ACL 2026 shared task for multimodal meme classification across En glish, Hindi, and Chinese. Our methodol ogy leverages a late-fusion multimodal archi tecture combining XLM-RoBERTa for textual features and ResNet-50 for visual representa tion. By utilizing weighted cross-entropy to address extreme class imbalance, we achieved Rank 3 in Chinese (F1: 0.7371), Rank 4 in English (F1: 0.6121), and Rank 7 in Hindi (F1: 0.1616). This paper details our system description and provides a critical analysis of the ”Hindi Failure,” where class imbalance led to a lack of model convergence.

Susmitha@LT-EDI 2026: Detecting LGBTQ+ Phobia in Multilingual Memes via Joint Representation

We present our approach to LT-EDI@ACL 2026 on counter-narrative generation for ho mophobic and transphobic comments. Gener ating high-quality counter-narratives in multi lingual and low-resource settings remains chal lenging, particularly when data imbalance and script variation affect model performance. To address these issues, we explore multiple mod eling strategies built around Gemma 3 12B with QLoRA fine-tuning, including data rebalanc ing and alternative input strategies for Tamil. Our findings show that task-specific fine-tuning combined with native-script Tamil produces more stable and higher-quality outputs than large few-shot prompts or transliteration-based inputs. On the official leaderboard, our system ranks second in English with an overall score of 86.35% and sixth in Tamil with 63.77%, highlighting both the effectiveness of targeted fine-tuning and the challenges of low-resource counter-narrative generation.

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