LoRIF reduces storage and query latency for gradient-based training data attribution from O(D) to O(c sqrt(D)) per sample and Hessian memory from O(D^2) to O(Dr) while preserving attribution quality on models up to 70B parameters.
This will help her determine if the rectangle is a square or not, but it will not directly give her the side length
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LoRIF: Low-Rank Influence Functions for Scalable Training Data Attribution
LoRIF reduces storage and query latency for gradient-based training data attribution from O(D) to O(c sqrt(D)) per sample and Hessian memory from O(D^2) to O(Dr) while preserving attribution quality on models up to 70B parameters.