Online platforms increasingly host hate speechtargeting marginalized communities, includ-ing homophobic and transphobic commentsdirected 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 Taskon Counter-Narrative Generation on Homopho-bic and Transphobic Comments at LT-EDI @ACL 2026. We adopt a zero-shot promptingapproach using large language models accessedthrough publicly available AI tools, includingGPT-4o, Gemini 1.5 Pro, and Llama-3 SonarLarge via Perplexity AI. Instead of traininga task-specific model, we design a structuredprompt that guides the models to generate re-spectful, concise, and contextually appropriatecounter-narratives.Experiments were conducted on English andTamil comments provided by the organiz-ers. Results demonstrate that prompt-basedgeneration can produce meaningful multilin-gual counter-narratives without additional train-ing. Our approach highlights the potential oflarge language models as lightweight tools forcounter-speech generation in multilingual on-line environments.

This paper presents our submission to the De-pression Detection in Dravidian Languagesshared task at DravidianLangTech 2026. Weinvestigate three complementary approachesfor speech-based depression detection in Tamiland Malayalam: (i) acoustic feature engineer-ing using MFCC and prosodic features with aSupport Vector Machine (SVM) classifier, (ii)a convolutional neural network (CNN) trainedon Mel-spectrogram representations, and (iii)a transformer-based model using Whisper-generated transcripts fine-tuned with XLM-RoBERTa. Experimental results show thatacoustic feature-based SVM and spectrogram-based CNN models achieve the strongestperformance on both Tamil and Malayalamdatasets, while the transformer-based approachalso produces competitive results. We furtherdiscuss limitations and future research direc-tions.