#instruction-tuning3 articles

3 articles

AIBullisharXiv – CS AI · 5h ago1

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FCN-LLM: Empower LLM for Brain Functional Connectivity Network Understanding via Graph-level Multi-task Instruction Tuning

Researchers have developed FCN-LLM, a framework that enables Large Language Models to understand brain functional connectivity networks from fMRI scans through multi-task instruction tuning. The system uses a multi-scale encoder to capture brain features and demonstrates strong zero-shot generalization across unseen datasets, outperforming conventional supervised models.

AIBullisharXiv – CS AI · 5h ago1

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VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning

Researchers developed VisNec, a framework that identifies which training samples truly require visual reasoning for multimodal AI instruction tuning. The method achieves equivalent performance using only 15% of training data by filtering out visually redundant samples, potentially making multimodal AI training more efficient.

AIBullisharXiv – CS AI · 5h ago1

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Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain

Researchers developed a method for creating synthetic instruction datasets to improve domain-specific LLMs, demonstrating with a 9.5 billion token Japanese financial dataset. The approach enhances both domain expertise and reasoning capabilities, with models and datasets being open-sourced for broader use.