Berkeley AI Research proposes SPEX and ProxySPEX for identifying key LLM interactions at scale
Identifying Interactions at Scale for LLMs
What happened
Berkeley AI Research has proposed SPEX and ProxySPEX, algorithms that identify important interactions driving LLM outputs across feature, data, and model-component attribution. SPEX turns interaction search into a sparse-recovery problem by exploiting sparsity and low-order structure; ProxySPEX uses additional hierarchy to match SPEX performance with about ten times fewer ablation experiments.
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