RASLIK uses randomized antipodal search on linearized influence kernels to achieve data Pareto improvement in LLM unlearning, outperforming baselines with sublinear complexity and double gains in quality and efficiency.
Axiomatic attribution for deep networks
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A lightweight max-pooling network with MLP detects LLM hallucinations competitively without semantic consistency computations by adaptively aggregating internal token features.
FAMPE is a new attribution method that applies FFT-based frequency-selective perturbations integrated with model parameter exploration to produce fine-grained feature importance maps, showing gains over AttEXplore on ImageNet.
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Randomized Antipodal Search Done Right for Data Pareto Improvement of LLM Unlearning
RASLIK uses randomized antipodal search on linearized influence kernels to achieve data Pareto improvement in LLM unlearning, outperforming baselines with sublinear complexity and double gains in quality and efficiency.
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Max-pooling Network Revisited: Analyzing the Role of Semantic Probability in Multiple Instance Learning for Hallucination Detection
A lightweight max-pooling network with MLP detects LLM hallucinations competitively without semantic consistency computations by adaptively aggregating internal token features.
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Frequency-Aware Model Parameter Explorer: A new attribution method for improving explainability
FAMPE is a new attribution method that applies FFT-based frequency-selective perturbations integrated with model parameter exploration to produce fine-grained feature importance maps, showing gains over AttEXplore on ImageNet.