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This paper explores improving cross-lingual transfer for sequential sentence classification in research papers by leveraging structural similarity, proposing methods that enhance performance in multilingual settings based on experiments with encoder-based and generative models.
IKS-Instruct is a multilingual dataset of 24,795 instruction-response pairs for teaching language models Indian Knowledge Systems, spanning seven Indian languages and covering 41 pedagogical techniques. Evaluation shows a fine-tuned 7B model performs competitively with larger general-purpose models on IKS-specific tasks.