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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 4·5 min read
Visualising Information Flow in Word Embeddings with Diffusion Tensor Imaging
TL;DR
A novel tool using diffusion tensor imaging (DTI) visualizes information flow in word embeddings from large language models (LLMs), enhancing the understanding of natural language context. This method reveals how representations change between tokens, improving interpretability and model comparison.
✦ Why It Matters
Implement DTI visualization to better understand and optimize your LLMs today.
Key Takeaways
- Explore DTI to visualize information flow in your LLMs.
- Use insights from DTI to improve model interpretability.
- Consider pruning under-utilized layers based on DTI findings.
- Shift focus from isolated words to full expressions in NLP.
How It Works
Diffusion tensor imaging (DTI) is applied to word embeddings to visualize how representations evolve between tokens in LLMs. By analyzing the changes in embedding space across different layers, researchers can track the flow of information and understand the contextual relationships between words in natural language expressions.