Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation
Year: 2026
Type: conference-paper
Abstract: Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each model explains only its own activations.We introduce Universal Activation Verbalizer (UAV), a shared-decoder framework that uses donor-and layer-specific adapters to explain activations from heterogeneous donor models.UAV learns a lightweight adapter that converts donor activations into soft tokens in decoder's embedding space, and further supports adapter-only transfer by reusing a frozen decoder-side LoRA while training only a new adapter for another donor.Across classification, fact retrieval, and gist summarization, UAV remains competitive with strong self-explanation baselines while enabling cross-model verbalization across model families and scales.Ablations show that decoder-side tuning mainly improves task behavior, whereas the adapter provides the activation-grounded factual and semantic evidence.Code and data are available at our GitHub repository.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7162394530