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Hierarchical modeling in RAG frameworks can drastically enhance MCQ generation and answer prediction in low-resource languages like Bengali.
Standard knowledge distillation can harm model performance, but new reliability-aware methods can boost summarization accuracy by over 50 times the improvement of traditional approaches.
Task-specific LLMs aren't just smaller versions of general models; they rely on a small subset of neurons so critical that removing just 10% can completely break them.
