Online platforms continue to witness harmful expressions targeting LGBTQ+ individuals, particularly in the form of homophobic and transphobic comments. While detection of such content has received substantial attention, generating constructive counter-narratives remains comparatively underexplored. In this shared task, we focus on counter-narrative generation in English and Tamil. Participants were provided with social media comments labeled as homophobic or transphobic and were required to generate respectful, contextually appropriate responses that challenge prejudice and promote empathy. Systems were evaluated using both reference-based metrics (Distinct-2 and BERTScore-F1) and rubric-based human evaluation metrics measuring politeness (PRS), quality (QS), and contextual coherence (CCNC). The results demonstrate variation in system performance across languages, with English systems showing stronger lexical diversity and Tamil systems excelling in politeness and contextual coherence. This paper presents dataset statistics, evaluation methodology, system performance analysis, and key observations from the shared task.

Chatbot is defined as one of the most advanced and promising expressions of interaction between humans and machines. They are sometimes called as digital assistants that can analyze human capabilities. There are so many chatbots already developed in English with supporting libraries and packages. But to customize these engines in other languages is a tedious process. Also there are many barriers to train these engines with other morphologically rich languages. Artificial Intelligence (AI) based or Machine Learning based Chatbots can answer complex ambiguous questions. The AI chatbots are capable of creating replies from scratch using Natural Language Processing techniques. Both categories have their advantages and disadvantages. Rule based chatbots can give more reliable and grammatically correct answers but fail to respond to questions outside their knowledge base. On the other hand, machine learning based chatbots need a vast amount of learning data and necessitated continuous improvement to the data-base to improve the cognitive capabilities.A hybrid chatbot employs the concepts of both AI and rule based bots, it can handle situations with both the approaches. One of the biggest threat faced by the society during the Corona pandemic was Mis-Information, Dis-information and Mal- information. Government wanted to establish a single source of truth, where the public can rely for authentic information. To support the cause and to fulfill the need to support the general public due to the rapid spread of COVID-19 Pandemic during the months of February and March 2020, ICFOSS has developed an interactive bot which is based on ‘hybrid technology’ and interacts with the people in regional language (Malayalam).