Formulates context attribution as a combinatorial multi-armed bandit problem solved via Linear Thompson Sampling to reduce LLM queries by up to 30% on QA benchmarks while matching existing attribution quality.
Ada-sise: adaptive semantic input sampling for efficient explanation of convolutional neural networks
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Context Attribution with Multi-Armed Bandit Optimization
Formulates context attribution as a combinatorial multi-armed bandit problem solved via Linear Thompson Sampling to reduce LLM queries by up to 30% on QA benchmarks while matching existing attribution quality.