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Dr. Tanaka's presentation on cognitive frameworks with ML approaches.
Reference: Tanaka et al. (2024)1
1. "Cognitive-Inspired Attention in LLMs"
Multi-modal learning involves training models on multiple types of data inputs (text, images, audio) simultaneously.
This helps models develop more robust representations by connecting concepts across different modalities.
The most promising aspect is how attention mechanisms in LLMs can be improved by cognitive science principles.
私たちの研究では、人間の視覚的注意の仕組みをモデルに組み込むことで、パフォーマンスが向上することが示されています。
(Translation: Our research shows that incorporating human visual attention mechanisms improves performance.)
Have you considered how this might apply to cross-modal learning?
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