This paper describes the system developed byTeamV for the LT-EDI 2026 Shared Task onCounter-Narrative Generation on Homophobic Transphobic Comments. The shared taskcomprises two subtasks: (1) Hate Speech SpanDetection in English, Tamil, and Hindi, and (2)Counter-Narrative Generation in English andTamil. Our system leverages the reasoning andmultilingual capabilities of a large proprietarylanguage 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 Placeacross all three languages, securing macro F1scores of 0.5338 in English, 0.5272 in Tamil,and 0.5478 in Hindi. For Task 2, our generatedcounter-narratives ranked 1st globally in En-glish with an overall average score of 87.47%and 5th in Tamil. We present our promptingmethodology, robust span-matching pipeline,detailed official results, and an analysis of themodel’s performance across diverse languages.

This paper describes our system submitted to the DravidianLangTech@ACL 2026 shared task on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments. The task requires classifying Tamil political tweets into seven sentiment categories. We address two key challenges, severe class imbalance and semantic overlap between categories, through a three-stage pipeline. First, we balance the training set by augmenting minority classes via back-translation and transformer-based paraphrasing. Second, we fine-tune XLM-RoBERTa-base using a class-weighted Focal Loss (𝛾=2), which directs learning towards hard, ambiguous samples. Third, we train five models under Stratified 5-Fold Cross-Validation and average their softmax outputs at inference time. On the official test set, the system achieves a Macro-F1 of 0.3539. The code is publicly available at: https://github.com/meclin2345/PolyTicsTamil_Alchemists

This paper describes our system submitted to the shared task on Hope Speech Detection in Tulu at DravidianLangTech@ACL 2026 hope-speech-dravidianlangtech-acl-2026. The task comprises two sub-tasks: coarse-grained classification into four categories Task 1 and fine-grained classification into five categories Task 2. We compare a traditional TF-IDF + LinearSVC baseline against XLM-RoBERTa fine-tuned with minority-class oversampling and Focal Loss. Our experiments reveal an interesting trade-off: while the transformer approach achieves the best validation Macro-F1 of 0.57 on the coarse-grained task, the TF-IDF baseline outperforms it on the smaller fine-grained task, highlighting the data scarcity threshold below which large pre-trained models struggle to generalise. On the official test set, our system achieves a Macro-F1 of 0.55 on Task 1 and 0.40 on Task 2. The code is publicly available at: https://github.com/meclin2345/Hope_Speech_Alchemists

This paper describes our system submitted to the shared task on Abusive Tamil Text Targeting Women on Social Media at DravidianLangTech@ACL 2026. We formulate the problem as a supervised binary classification task, assigning each Tamil social media comment to an Abusive or Non-Abusive category. Our pipeline begins with a tailored preprocessing stage that handles emoji translation, URL removal, and entity normalization. We then independently fine-tune two pre-trained transformer models MuRIL and XLM-RoBERTa on the task data. At inference time, we combine these models through a weighted softmax ensemble, assigning a weight of 0.6 to MuRIL and 0.4 to XLM-RoBERTa. The resulting system achieves a Macro-F1 score of 0.8115 on the test set, outperforming both individual models. The code is publicly available at: https://github.com/meclin2345/AbuseDetect_Alchemists

United Nations and UNESCO resolutions encode complex collective reasoning through highly structured preambles and operative clauses. Reconstructing this implicit argumentative structure is a challenging natural language processing task. This paper describes our submission to the UZH Shared Task at the ArgMining Workshop 2026. Adhering to the strict constraint of using open-weight models with at most 8B parameters, we propose a highly efficient, modular pipeline built entirely upon the Qwen-2.5-7B-Instruct architecture. To address Subtask 1, we decouple the problem, employing a 4-bit quantized LoRA adapter via the Unsloth framework for paragraph type classification and a type-informed chain-of-thought approach for thematic tagging and relation prediction.

This study addresses the Shared Task on Patient-Centric Multilingual Question Answering, which focuses on generating summaries and patient-oriented answers from multi-turn medical dialogues related to Head and Neck Cancer and Cystic Fibrosis across ten languages. The Qwen3-1.7B model is fine-tuned using QLoRA for three tasks—Summarization, Question Answering, and Information Extraction—while updating only approximately 1.6% of parameters through task-specific adapter layers. The resulting system demonstrates strong semantic fidelity, as evidenced by high BERTScore and COMET scores, particularly for Kannada, English, Telugu, and Tamil, with comparatively lower performance in Assamese, Bangla, Gujarati, and Marathi. The modular fine-tuning design enables efficient task adaptation while satisfying the constraints on model size and computational resources.