The deployment of Large Language Models(LLMs) has intensified concerns regarding the propagation of societal stereotypes encoded with webscale training corpora. This paper presents a dual-paradigm framework specially designed to address multilingual gender-inclusvity and counterfactual generation. For multilingual gender-neutral text transformation,a fine-tuned mT5 encoder–decoder model performs controlled sentence rewriting with minimal edits while preserving semantic fidelity and grammatical fluency. For counter-narrative generation, the Llama-3 8B decoder-only model is employed to generate empathetic and persuasive responses through structured prompt-based generation. The framework is evaluated using datasets from the LT-EDI ACL 2026 sharedtask across multiple languages, including English, Tamil, Kannada, German, and Spanish. Experimental results demonstrate strong effectiveness in identifying and neutralizing gender markers, particularly in morphologically rich languages, while the counter-narrative component achieves high performance in politeness, coherence, and relevance. Overall, the proposed approach contributes toward the development of responsible and inclusive multilingual NLP systems.

We present a universal Parts-of-Speech (POS) tagset framework covering most of the Indian languages (ILs) following the hierarchical and decomposable tagset schema. In spite of significant number of speakers, there is no workable POS tagset and tagger for most ILs, which serve as fundamental building blocks for NLP research. Existing IL POS tagsets are often designed for a specific language; the few that have been designed for multiple languages cover only shallow linguistic features ignoring linguistic richness and the idiosyncrasies. The new framework that is proposed here addresses these deficiencies in an efficient and principled manner. We follow a hierarchical schema similar to that of EAGLES and this enables the framework to be flexible enough to capture rich features of a language/ language family, even while capturing the shared linguistic structures in a methodical way. The proposed common framework further facilitates the sharing and reusability of scarce resources in these languages and ensures cross-linguistic compatibility.