United States

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.

ACL 2026

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

counterfactual bias mitigation

gender-inclusive rewriting

gender bias

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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.

SigJBS@LT-EDI 2026: QLoRA-Tuned Homophobic and Transphobic Counter Narrative Generation

This paper describes the system developed by TeamV for the LT-EDI 2026 Shared Task on Counter-Narrative Generation on Homophobic & Transphobic Comments. The shared task comprises two subtasks: (1) Hate Speech Span Detection in English, Tamil, and Hindi, and (2) Counter-Narrative Generation in English and Tamil. Our system leverages the reasoning and multilingual capabilities of a large proprietary language model (Qwen3-Max) through rigor ous few-shot in-context learning (ICL) and ro bust post-processing mechanisms. Our submit ted system demonstrated state-of-the-art perfor mance on the official CodaBench leaderboard. In Task 1, our approach achieved 1st Place across all three languages, securing macro F1 scores of 0.5338 in English, 0.5272 in Tamil, and 0.5478 in Hindi. For Task 2, our generated counter-narratives ranked 1st globally in En glish with an overall average score of 87.47% and 5th in Tamil. We present our prompting methodology, robust span-matching pipeline, detailed official results, and an analysis of the model’s performance across diverse languages.

TeamV at LT-EDI 2026: Multilingual Hate Speech Span Detection and Counter-Narrative Generation via Few-Shot In-Context Learning

The "law of conformity," the finding that frequent words are semantically stable, has been treated as a broad regularity of language change. We show it does not hold for Korean. Using diachronic word embeddings trained on historical corpora spanning 500 years (15th–20th centuries), we find a robust positive correlation between frequency and semantic shift: high-frequency Korean words change more, not less. The pattern survives six robustness controls and is validated against an English replication. Partial correlation analysis reveals that the role of polysemy in mediating the frequency–change relationship is not fixed but depends on time resolution and corpus homogeneity. We connect the reversal to frequency-driven reductive processes, including grammaticalization, semantic bleaching, and domain shift, that are especially productive in Korean. The frequency–change relationship is not a fixed regularity but varies with language typology and analytical conditions.

Frequency Accelerates Semantic Change: Evidence from 500 Years of Korean

This study proposes a quantitative framework for profiling LLM dispositions as stable, model-specific regularities in output under repeated, controlled elicitation. Using a structured narrative constraint-selection task administered across six frontier models and three instruction types, we operationalize disposition through two dimensions: "consistency", measured as cross-replication selection overlap via Jaccard similarity, and "diversity", measured as dispersion across options via the inverse Simpson index. We further introduce Narrative Landscape, a PCA-based visualization that maps each model's selection profile into a shared space for direct comparison. Results reveal a clear rigidity-exploration spectrum across model families and show that instruction types shift the geometry of selection spaces even when scalar metrics appear similar, indicating that comparable scores can mask qualitatively distinct selection topologies.

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