MAMMOTH: Massive multimodal helper for multi-discipline reasoning
By Robert Kim, Meera Nair, Sofia Rodriguez
"Presents a multimodal assistant trained on complex scientific datasets. Shows significant gains in graphical reasoning and visual instruction following."
Abstract
We present MAMMOTH, a multimodal architecture trained on extensive mathematical, scientific, and document parsing instruction sets. MAMMOTH sets new benchmarks in multi-turn multi-modal reasoning and chart understanding.
Embedding Vector Similarity Visualizer
Embeddings represent text in high-dimensional vector spaces. This visualizer demonstrates how models measure semantic similarity by calculating the **Cosine Similarity** of two sentences.
Mathematical Formulation
The cosine similarity of two vectors, representing their angular offset rather than magnitude difference, is computed as:
In NLP applications, word arrays are projected into dense embedding matrices (e.g. 1536 dimensions). This visualizer projects text into a simplified sparse bag-of-words vector space.
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