ColNanoVDR is a framework for document-free query distillation in multi-vector visual document retrieval, using optimal transport with learned weights to align student and teacher model embeddings without requiring page-level data. It achieves high performance with significantly faster query encoding and reduced data usage compared to traditional methods.
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This approach enables faster and more efficient query processing in document retrieval systems without relying on page-level data, reducing computational and storage demands.
The paper explores the linearity in Large Language Models (LLMs) by showing that combining inputs from different text streams leads to a superposition of next-token distributions. It suggests that this linearity is an inherent property of the Transformer architecture and can be restored through fine-tuning, allowing for generating two coherent continuations from one forward pass.
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6 days ago